US 20020051433 A1 Abstract A receiver of the present invention addresses the need for improved interference suppression without the number of transmissions by the power control system being increased, and, to this end, provides a receiver for a CDMA communications system which employs interference subspace rejection to tune a substantially null response to interference components from selected signals of other user stations. Preferably, the receiver also tunes a substantially unity response for a propagation channel via which a corresponding user's signal was received. The receiver may be used in a base station or in a user/mobile station.
Claims(46) 1. A receiver suitable for a base station of a CDMA communications system comprising at least one base station (11) having a transmitter and a said receiver and a multiplicity (U) of user stations (10 ^{1}, . . . , 10 ^{U}) including a plurality (U′) of user stations served by said at least one base station, each user station having a transmitter and a receiver for communicating with said at least one base station via a corresponding one of a plurality of channels (14 ^{1}, . . . , 14 ^{U}), the base station receiver for receiving a signal (X(t)) comprising components corresponding to spread signals transmitted by the transmitters of the plurality of user stations, each of said spread signals comprising a series of symbols spread using a spreading code unique to the corresponding user station, said base station receiver comprising:
a plurality (U′) of receiver modules ( 20 ^{1}, . . . , 20 ^{NI}, 20 ^{d}) each for deriving from successive frames of the received signal (X(t)) estimates of said series of symbols of a corresponding one of the user stations, preprocessing means ( 18) for deriving from the received signal (X(t)) a series of observation matrices (Y_{n}) each for use by each of the receiver modules (20) in a said frame to derive an estimate of a symbol of a respective one of said series of symbols, and means ( 19,44;44/1,44/2) for deriving from each observation matrix a plurality of observation vectors (Y _{n}; Y _{n−1}; Z _{n} ^{1 }. . . Z _{n} ^{NI}; Z _{n} ^{d}) and applying each of the observation vectors to a respective one of the plurality of receiver modules (20 ^{1}, . . . , 20 ^{NI}, 20 ^{d}); each receiver module comprising;
channel identification means (
28) for deriving from one of the observation vectors a channel vector estimate (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}; Ŷ _{0,n} ^{d}; {circumflex over (Y)}_{0,n−1} ^{i}) based upon parameter estimates of the channel between the base station receiver and the corresponding user station transmitter; beamformer means (
27 ^{1}, . . . , 27 ^{NI}, 27 ^{d}; 47 ^{d}) having coefficient tuning means (50) for producing a set of weighting coefficients in dependence upon the channel vector estimate, and combining means (51,52) for using the weighting coefficients to weight respective ones of the elements of a respective one of the observation vectors and combining the weighted elements to provide a signal component estimate (ŝ_{n} ^{1}, . . . , ŝ_{n} ^{U}); and symbol estimating means (29 ^{1}, . . . , 29 ^{U}, 30 ^{1}, . . . , 30 ^{U}) for deriving from the signal component estimate an estimate ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{U}) of a symbol (b_{n} ^{1}, . . . , b_{n} ^{U}) transmitted by a corresponding one of the user stations (10 ^{1}, . . . , 10 ^{U}), wherein said receiver further comprises means ( 42,43) responsive to symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}; g^{1}, g^{2}, g^{3}; g^{l} ^{ −1,n }) and to channel estimates ( _{n} ^{1 }. . . _{n} ^{NI}; _{n−1} ^{i}) comprising at least said channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}) for channels (14 ^{1}, . . . , 14 ^{NI}) of a first group (I) of said plurality of user stations (10 ^{1}, . . . , 10 ^{NI}) to provide at least one constraint matrix (Ĉ_{n}) representing interference subspace of components of the received signal corresponding to said predetermined group, and in each of one or more receiver modules (20A^{d}) of a second group (D) of said plurality of receiver modules, the coefficient tuning means (50A^{d}) produces said set of weighting coefficients in dependence upon both the constraint matrix (Ĉ_{n}) and the channel vector estimates (Ĥ _{n} ^{d}) so as to tune said one or more receiver modules (20A^{d}) each towards a substantially null response to that portion of the received signal (X(t)) corresponding to said interference subspace. 2. A receiver according to 50A^{d}) also tune said one or more receiver nodules (20A^{d}), respectively, each towards a substantially unity response for the component of the received signal (X(t)) from the transmitter of the corresponding one of the user stations. 3. A receiver according to 19;44) comprises first reshaping means (44) for reshaping the observation matrix (Y_{n}) from the preprocessing means (18) and supplying the resulting observation vector (Y _{n}) to said beamformer means (47A^{d}) of each of said one or more receiver modules (20A^{d}), and wherein the means (42, 43) for providing said at least one constraint matrix comprises constraints-set generating means (42A) responsive to said channel estimates ( _{n} ^{l}, . . . , _{n} ^{NI}; _{n−1} ^{i}) symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}; g^{1}, g^{2}, g^{3}; g^{l} ^{ −1,n }) corresponding to said first group for generating a plurality of constraints-set matrices ( _{n} ^{1}, . . . , _{n} ^{N} ^{ c }) together characterizing the subspace of interference attributable to said first group of user stations and the constraint matrix generating means (43A) comprises a bank of vector reshapers (48A^{1}, . . . , 48A^{N} ^{ c }) for reshaping the constraints-set matrices ( _{n} ^{1}, . . . , _{n} ^{N} ^{ c }) to form columns, respectively, of the constraint matrix (Ĉ_{n}), the constraint matrix generating means (43A) supplying the constraint matrix to each of said coefficient tuning means (50A^{d}) of said one or more receiver modules (20 ^{d}) of said second group, and wherein, in each of said one or more receiver modules (20A^{d}), the channel estimation means (28A^{d}) supplies spread channel vector estimates (Ŷ _{0,n} ^{d}) to the coefficient tuning means (50A^{d}) for use in updating said weighting coefficients. 4. A receiver according to 43A) comprises transformation means (49A) for forming an inverse matrix (Q _{n}) in dependence upon the constraint matrix (Ĉ_{n}) and supplying said inverse matrix to said coefficient tuning means (50A^{d}) of said one or more receiver modules (20A^{d}), and wherein said coefficient tuning means (50A^{d}) computes said weighting coefficients in dependence upon said constraint matrix, said inverse matrix and said channel vector estimate. 5. A receiver according to 19 ^{1}, . . . , 19 ^{NI}, 19 ^{d}) each for using a corresponding one of the user spreading codes to despread the observation matrix (Y_{n}) using a respective one of the spreading codes to form a user-specific post-correlation vector (Z _{n} ^{1}, . . . , Z _{n} ^{NI}, Z _{n} ^{d}) and supplying same to a respective one of the channel identification means (28 ^{1}, . . . , 28 ^{NI}, 28 ^{d}). 6. A receiver according to 30 ^{1}, . . . , 30 ^{NI}) for deriving amplitude estimates ({circumflex over (ψ)}_{n} ^{1}, . . . , {circumflex over (ψ)}_{n} ^{NI}) of signal components from said first group of user stations and supplying the amplitude estimates to said constraints-set generating means as parts of said channel estimates, and wherein the constraints-set generating means (42C) comprises a plurality of respreaders (57C^{1}, . . . , 57C^{NI}) each for using a corresponding one of the user spreading codes to respread a respective one of the symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}) from the receiver modules corresponding to said first group (I) of user stations, scaling means (58C^{1}, . . . , 58C^{NI}) for scaling the respread symbol estimates by the amplitudes ({circumflex over (ψ)}_{n} ^{1}, . . . , {circumflex over (ψ)}_{n} ^{NI}) of the signal components corresponding to the symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}), respectively, and a plurality of channel replication means (59C^{1}, . . . , 59C^{NI}) having coefficients adjustable in dependence upon the channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), respectively, for filtering the corresponding respread and scaled symbol estimates to provide user-specific observation matrix estimates (Ŷ_{n−1} ^{1}, . . . , Ŷ_{n−1} ^{NI}), respectively, and means (60) for summing the user-specific observation matrices to form an observation matrix estimate (Î_{n−1}) and supplying same to the constraint matrix generator means (43C), the constraint matrix generator means (43C) comprising vector reshaping means for reshaping the observation matrix estimate (Î_{n−1}) to form an observation vector estimate (Î _{n−1}) as a single column constraint matrix (Ĉ_{n}) for application to the coefficient tuning means (50A^{d}) of each of said receiver modules (20 ^{d}) of said second group (D). 7. A receiver according to 42D) generates a number of constraints (N_{c}) equal to the number (NI) of user stations in said first group (I) and comprises a plurality of respreaders (57D^{1}, . . . 3,57D^{NI}) each for using a corresponding one of the user spreading codes to respread a respective one of the symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}) from a predetermined group (I) of said receiver modules corresponding to said selected ones of said components of the received signals, and a plurality of channel replication means (59D^{1}, . . . , 59D^{NI}) having coefficients adjustable in dependence upon the channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), respectively, for filtering the corresponding respread symbol estimates to provide a plurality of user-specific observation matrix estimates (Ŷ_{n−1} ^{1}, . . . , Ŷ_{n−1} ^{NI}) respectively, and wherein, in the constraint matrix generator (43D), the bank of vector reshapers (48A^{1}, . . . , 48A^{N} ^{ d }) reshape the user-specific observation matrix estimates to form a plurality of user-specific observation vector estimates (Ŷ _{n−1} ^{1}, . . . , Ŷ _{n−1} ^{NI}), respectively, as respective columns of the constraint matrix (Ĉ_{n}) for supply to each of the coefficient tuning means (50A^{d}) of each of said receiver modules (20 ^{d}) of said second group (D). 8. A receiver according to 28 ^{1}, . . . , 28 ^{NI}) of said first group of receiver modules (20E^{1}, . . . , 20E^{NI}) provide both the channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), respectively, and a plurality of sets of sub-channel vector estimates (Ĥ_{n} ^{1,1}, . . . , Ĥ_{n} ^{1,N} ^{ f }, . . . , Ĥ_{n} ^{NI,1}, . . . , Ĥ_{n} ^{NI,N} ^{ f }), respectively, each of the channel estimates ( _{n} ^{1}, . . . , _{n} ^{NI}) comprising a respective one of the sets of sub-channel vector estimates, each of the sets of sub-channel vector estimates representing an estimate of the channel parameters of N_{f }sub-channels of said channel between the base station and the transmitter of the corresponding one of the NI user stations in said first group, the constraints-set generator means (42E) comprises a plurality of respreaders (57E^{1}, . . . , 57E^{NI}) and a plurality of channel replicators (59E^{1}, . . . , 59E^{NI}) coupled to the plurality of respreaders (57E^{1}, . . . , 57E^{NI}), respectively, for filtering the plurality of respread symbols ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}), respectively, using respective ones of the sub-channel vector estimates (Ĥ_{n} ^{1,1}, . . . , Ĥ_{n} ^{1,N} ^{ f }, . . . , Ĥ_{n} ^{NI,1}, . . . , Ĥ_{n} ^{NI,N} ^{ f }) to form a plurality (N_{c}) of constraints equal in number to N_{f}NI and forming a plurality of user-specific sub-channel observation matrix estimates (Ŷ_{n−1} ^{1,1}, . . . , Ŷ_{n−1} ^{1,N} ^{ f }, . . . , Ŷ_{n−1} ^{NI,1}, . . . , Ŷ_{n−1} ^{NI,N} ^{ f }) corresponding to the sub-channels, respectively, and, in the constraint matrix generator (43E), the bank of reshapers (48A^{1}, . . . , 48A^{N} ^{ c }) reshape the sets of user-specific observation matrix estimates to form a corresponding plurality of sets of user-specific sub-channel observation vector estimates (Ŷ _{n−1} ^{1,1}, . . . , Ŷ _{n−1} ^{1,N} ^{ f }, . . . , Ŷ _{n−1} ^{NI,1}, . . . , Ŷ _{n−1} ^{NI,N} ^{ f }) as respective columns of the constraint matrix (Ĉ_{n}) for supply to each of the coefficient tuning means (50A^{d}) of each of said receiver modules (20 ^{d}) of said second group (D). 9. A receiver according to 63F^{1}, . . . , 63F^{NI}) for generating for each of said selected ones of said components a series of hypothetical symbol estimates (g_{n} ^{1}, g_{n} ^{2}, g_{n} ^{3}), wherein the constraints-set generating means (42F) comprises a plurality of respreaders (57F^{1}, . . . , 57F^{NI}) each for respreading, using a corresponding one of the user spreading codes, selected sets of said hypothetical symbol estimates, and a plurality of channel replicator means (59F^{1}, . . . , 59F^{NI}), respectively, for filtering the sets of respread symbol estimates, each to form a plurality (N_{c}) of constraints equal in number to 3 NI and forming a plurality of user-specific observation matrix estimates (Ŷ_{0,n} ^{1}, Ŷ_{−1,n} ^{1}, Ŷ_{+1,n} ^{1}, . . . , Ŷ_{0,n} ^{NI}, Ŷ_{−1,n} ^{NI}, Ŷ_{+1,n} ^{NI}), the plurality of channel replication means (59F^{1}, . . . , 59F^{NI}) having coefficients adjustable in dependence upon the channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), respectively, and, in the constraint matrix generating means (43F), the bank of reshapers (48A^{1}, . . . , 48A^{N} ^{ c }) reshape the sets of user-specific observation matrix estimates to form a plurality of user-specific observation vector estimates (Ŷ _{0,n} ^{1}, Ŷ _{−1,n} ^{1}, Ŷ _{+1,n} ^{1}, . . . , Ŷ _{0,n} ^{NI}, Ŷ _{−1,n} ^{NI}, Ŷ _{+1,n} ^{NI}), respectively, as respective columns of the constraint matrix (Ĉ_{n}) for supply to each of the coefficient tuning means (50A^{d}) of each of said receiver modules (20 ^{d}) of said second group (D). 10. A receiver according to 63G^{1}, . . . , 63G^{NI}) for providing hypothetical symbol estimates (g_{n} ^{1} ^{ +1,n }) and wherein the constraints-set generating means uses a combination of said symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}) and said hypothetical symbol values in producing said set of user-specific observation matrix estimates. 11. A receiver according to 42G) comprises a plurality of respreaders (57G^{1}, . . . , 57G^{NI}) each for respreading, using a corresponding one of the user spreading codes, a respective one of the symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}) and a said hypothetical symbol estimates (g_{n} ^{1} ^{ −1,n }) to provide a plurality of respread symbol estimates, and a plurality of channel replicator means (59G^{1}, . . . , 59G^{NI}), respectively, for filtering the respread symbol estimates to form a plurality (N_{c}) of constraints equal in number to 2NI and forming a plurality of user-specific observation matrix estimates (Ŷ_{r,n} ^{1}, Ŷ_{+1,n} ^{1}, . . . , Ŷ_{r,n} ^{NI}, Ŷ_{+1,n} ^{NI}), the plurality of channel replication means (59F^{1}, . . . , 59F^{NI}) having coefficients adjustable in dependence upon the channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), respectively, and, in the constraint matrix generating means (43G), the bank of vector reshapers (48A^{1}, . . . , 48A^{N} ^{ c }) reshape the user-specific observation matrix estimates to form a corresponding plurality of user-specific observation vector estimates (Ŷ _{r,n} ^{1}, Ŷ _{+1,n} ^{1}, . . . , {circumflex over (Y)}_{r,n} ^{NI}, Ŷ _{+1,n} ^{NI}) respectively, as respective columns of the constraint matrix (Ĉ_{n}) for supply to each of the coefficient tuning means (50A^{d}) of each of said receiver modules (20 ^{d}) of said second group (D). 12. A receiver according to 19 ^{1}, . . . , 19 ^{NI}, 19 ^{d}) each for despreading the observation matrix (Y_{n}) using a corresponding one of the user spreading codes to produce a corresponding one of a plurality of post-correlation observation vectors (Z _{n} ^{1}, . . . , Z _{n} ^{NI}, Z _{n} ^{d}) and supplying the post-correlation observation vectors to both the channel identification means and the coefficient tuning means of the beamformer means of each of said one or more receiver modules, said elements of the observation vector weighted by said combining means of said one or more receiver modules being elements of the corresponding post-correlation observation vector, and wherein the constraint matrix providing means (42B, 43B) comprises means (42B) responsive to said channel estimates ( _{n} ^{1}, . . . , _{n} ^{NI}; _{n−1} ^{1}) and to symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}; g^{1}, g^{2}, g^{3}; g^{l+1,n}) corresponding to said first group (I) of user stations for providing a plurality of constraints-sets matrices ( _{n}) together characterizing the subspace of interference attributable to said spread signals of said first group of user stations, and constraint matrix generating means (43B) comprising a plurality of user-specific constraint matrix generators (43B^{d}) each associated with a respective one of said one or more receiver modules (20 ^{d}), each user-specific matrix generator (43B^{d}) having despreading means (55B^{d,1}, . . . , 55B^{d,N} ^{ r }) for using the corresponding user spreading code of the specific user to despread each of the user-specific constraints-set matrices respectively, to form a respective column of a corresponding one of a plurality of user-specific post-correlation constraint matrices (Ĉ_{PCM,n} ^{d}), the plurality of user-specific constraint matrix generating means (43B^{d}) supplying said plurality of user-specific post-correlation constraint matrices to the coefficient tuning means of the respective one of said one or more receiver modules (20B^{d}). 13. A receiver according to 30 ^{1}, . . . , 30 ^{NI}) for deriving amplitude estimates ({circumflex over (ψ)}_{n} ^{1}, . . . , {circumflex over (ψ)}_{n} ^{NI}) of signal component estimates of said first group of user stations and supplying the amplitude estimates to said constraints-set generating means as parts of said channel estimates, and wherein the constraints-set generating means (42C) comprises a plurality of respreaders (57C^{1}, . . . , 57C^{NI}) each for using a corresponding one of the user spreading codes to respread a respective one of the symbol estimates from receiver modules (20 ^{1}, . . . , 20 ^{NI}) corresponding to said first group of user stations, scaling means (58C^{1}, . . . , 58C^{NI}) for scaling each of the respread symbol estimates by said amplitudes ({circumflex over (ψ)}_{n} ^{1}, . . . , {circumflex over (ψ)}_{n} ^{NI}), respectively, a plurality of channel replication means (59C^{1}, . . . , 59C^{NI}) having coefficients adjustable in dependence upon the channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), respectively, for filtering the corresponding respread and scaled symbol estimates to provide user-specific observation matrix estimates (Ŷ_{n−1} ^{1}, . . . , Ŷ_{n−1} ^{NI}), respectively, and means (60) for summing the user-specific observation matrix estimates to form an observation matrix estimate (Î_{n−1}) and supplying same to each of the user-specific constraint matrix generators (43H^{d}) of said one or more receiver modules of said second group, each of the user-specific constraint matrix generators (43H^{d}) comprising despreading means (55B^{d}) for despreading the observation matrix estimate (Î_{n−1}) using the corresponding user spreading code to form a respective one (Î _{PCM,n−1} ^{d}) of a plurality of post-correlation user-specific observation vector estimates each as a single column respective constraint matrix (Ĉ_{PCM,n} ^{d}), for use by the associated one of the coefficient tuning means (50B^{d}) in said second group (D). 14. A receiver according to 42D) comprises a plurality of respreaders (57D^{1}, . . . , 57D^{NI}) each for respreading a respective one of the symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}) from a predetermined group (I) of said receiver modules corresponding to said selected ones of said components of the received signals, and a plurality of channel replication means (59D^{1}, . . . , 59D^{NI}) having coefficients adjustable in dependence upon the channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), respectively, for filtering the corresponding respread symbol estimates to provide a plurality of user-specific observation matrix estimates (Ŷ_{n−1} ^{1}, . . . , Ŷ_{n−1} ^{NI}) and wherein, in each of the user-specific constraint matrix generating means (43I^{d}), the despreading means (55B^{d,1}, . . . , 55B^{d,n} ^{ c }) despreads the user-specific observation matrix estimates to form a plurality of user-specific observation vector estimates (Ŷ _{n−1} ^{1}, . . . , Ŷ _{n−1} ^{NI}) respectively, as respective columns of a respective user-specific constraint matrix (Ĉ_{PCM,n}) for supply to the associated one of the coefficient tuning means (50B^{d}) of said one or more receiver modules (20 ^{d}) in said second group (D). 15. A receiver according to 28 ^{1}, . . . , 28 ^{NI}) of said first group (I) of receiver modules (20E^{1}, . . . , 20E^{NI}) generate both said channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), respectively, and a plurality of sets of sub-channel vector estimates (Ĥ_{n} ^{1,1}, . . . , Ĥ_{n} ^{1,N} ^{ f }, . . . , Ĥ_{n} ^{NI,1}, . . . , Ĥ_{n} ^{NI,N} ^{ f }), respectively, each of the channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), comprising a respective one of the sets of sub-channel vector estimates, each of the sets of sub-channel vector estimates representing an estimate of the channel parameters of sub-channels of said channel between the base station and the transmitter of the corresponding user station, the constraints-set generator means (42E) comprises a plurality of respreaders (57E^{1}, . . . , 57E^{NI}) and a plurality of channel replicators (59E^{1}, . . . , 59E^{NI}) coupled to the plurality of respreaders (57E^{1}, . . . , 57E^{NI}), respectively, for filtering the plurality of respread symbols ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}), respectively, of said first group using respective ones of the sub-channel vector estimates (Ĥ_{n} ^{1,1}, . . . , Ĥ_{n} ^{1,N} ^{ f }, . . . , Ĥ_{n} ^{NI,1}, . . . , Ĥ_{n} ^{NI,N} ^{ f }), to form a plurality of sets of user-specific observation matrix estimates (Ŷ_{n−1} ^{1,1}, . . . , Ŷ_{n−1} ^{1,N} ^{ f }, . . . Ŷ_{n−1} ^{NI,1}, . . . , Ŷ_{n−1} ^{NI,N} ^{ f }), the sets corresponding to the sub-channels, respectively, and, in each of the user-specific constraint matrix generators (43K^{d}), the despreading means (55B^{d,1}, . . . , 55B^{d,N} ^{ c }) despreads the sets of user-specific observation matrix estimates to form a corresponding plurality of sets of user-specific observation vector estimates (Î _{PCM,n−1} ^{1,1}, . . . , Î _{PCM,n−1} ^{d,NI,N} ^{ f }), the sets forming respective columns of a respective user-specific constraint matrix (Ĉ_{PCM,n} ^{d}) for supply to the associated one of the coefficient tuning means (50B^{d}) of the one or more receiver modules (20 ^{d}) in said second group (D). 16. A receiver according to 63L^{1}, . . . , 63L^{NI}) for generating for each of said selected ones of said components a series of hypothetical symbol estimates (g_{n} ^{1}, g_{n} ^{2}, g_{n} ^{3}), wherein the constraints-set generating means (42L) comprises respreading means (57L^{1}, . . . , 57L^{NI}) for respreading, using a corresponding one of the user spreading codes for said first group, selected sets of said hypothetical symbol estimates, and channel replicator means (59L^{1}, . . . , 59L^{NI}), respectively, for filtering the sets of respread hypothetical values, each to form one of a plurality of sets of user-specific observation matrix estimates (Ŷ_{0,n} ^{1}, Ŷ_{−1,n} ^{1}, Ŷ_{−1,n} ^{1}, . . . , Ŷ_{0,n} ^{NI}, Ŷ_{−1,n} ^{NI}, Ŷ_{+1,n} ^{NI}), the plurality of channel replication means (59L^{1}, . . . , 59L^{NI}) having coefficients adjustable in dependence upon the channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), respectively, and, in each of the user-specific constraint matrix generating means (43L^{d}), the despreading means (55B^{d,1}, . . . , 55B^{d,N} ^{ c }) despreads the sets of user-specific observation matrix estimates to form a corresponding plurality of sets of user-specific observation vector estimates (Î _{PCM,n} ^{d,1,1}, Î _{PCM,n} ^{d,1,2}, Î _{PCM,n} ^{d,1,3}, . . . , Î _{PCM,n} ^{d,NI,1}, Î _{PCM,n} ^{d,NI,2}, Î _{PCM,n} ^{d,NI,3}), respectively, as respective columns of a respective user-specific constraint matrix (Ĉ_{PCM,n} ^{d}) for supply to the coefficient tuning means (50B^{d}) of the associated one of said one or more receiver modules (20 ^{d}) in said second group (D). 17. A receiver according to 63M^{1}, . . . , 63M^{NI}) for providing hypothetical symbol estimates (g_{n} ^{l} ^{ n1,n }) and wherein the constraints-set generating means (42M) uses a combination of said symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}) and said hypothetical symbol estimates in producing said sets of user-specific observation matrix estimates. 18. A receiver according to 42M) comprises a respreading means (57M^{1}, . . . , 57M^{NI}) for respreading, using the symbol estimates ({circumflex over (b)}_{n} ^{1}, . . . , {circumflex over (b)}_{n} ^{NI}) and said hypothetical symbol estimates (g_{n} ^{i} ^{ +1,n }) using respective ones of the user spreading codes, to provide a plurality of respread symbol estimates, and channel replicator means (59M^{1}, . . . , 59M^{NI}) for filtering the respread symbol estimates to form a plurality of pairs of user-specific observation matrix estimates (Ŷ_{r,n} ^{1}, Ŷ_{+1,n} ^{1}, . . . , Ŷ_{r,n} ^{NI}, Ŷ_{+1,n} ^{NI}), each pair corresponding to one of said first group of user stations, the channel replication means (59M^{1}, . . . , 59M^{NI}) having coefficients adjustable in dependence upon the channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}), respectively, and, in each of the user-specific constraint matrix generating means (43M^{d}), the despreading means (55B^{d,1}, . . . , 55B^{d,N} ^{ c }) despreads the user-specific observation matrix estimates to form a corresponding plurality of user-specific observation vector estimates (Î _{PCM,n} ^{d,l,k} ^{ 1 }, Î _{PCM,n} ^{d,l,K} ^{ 2 }, . . . , Î _{PCM,n} ^{d,NI,k} ^{ 1 }, Î _{PCM,n} ^{d,NI,k} ^{ 2 }), respectively, as respective columns of a corresponding user-specific constraint matrix (Ĉ_{PCM,n}) for supply to the coefficient tuning means (50B^{d}) of the associated one of said one or more receiver modules (20 ^{d}) in said second group (D). 19. A receiver according to at least one ( 20 ^{i}) of said one or more receiver modules both supplies symbol estimates to said constraint-matrix generating means (42P,43P) for use in deriving said constraint matrices and uses said constraint matrices in tuning weighting coefficients of its beamformer means (47P^{i}), said at least one of said one or more receiver modules further comprising a second beamformer means ( 27P^{i}) having second combining means and second coefficient tuning means for tuning the weighting coefficients of the second combining means; the observation vector deriving means ( 19,44;44/1,44/2) further comprising second reshaping means (44/2) for reshaping said observation matrix (Y_{n}) and supplying a resulting second observation vector (Y _{n}) to said second combining means (27P^{i}) and delay means (45) for delaying the first observation vector (Y _{n−1}) relative to the second observation vector (Y _{n}); the second coefficient tuning means also being arranged to tune the weighting coefficients of the second combining means in dependence upon the channel vector estimate ( Ŷ _{0,n−1} ^{i}) used by the first coefficient tuning means in beamformer (47P^{i}); and wherein
the channel identification means (
28P^{i}) of said at least one of the one or more receiver modules derives the channel vector estimate (Ŷ _{0,n−1} ^{i}) from the delayed first observation vector (Y _{n−1}) and supplies said channel vector estimate to the respective coefficient tuning means of the first combining means and the second combining means for use in updating their weighting coefficients and supplies to the constraint matrix generator means (42P,43P) said channel estimates ( _{n−1} ^{l}) for use in deriving the constraint matrix; the first combining means ( 47P^{i};51,52) and second combining means (27P^{i};51,52) use their respective weighting coefficients to weight respective ones of the elements of the delayed first observation vector and the second observation vector, respectively, and combine the weighted elements of the respective first and second observation vectors to provide first signal component estimate (ŝ_{n−1} ^{i}) and second signal component estimate (ŝ_{MRC,n} ^{i}) respectively; said at least one of said one or more receiver modules further comprises second symbol estimating means ( 29P/2 ^{i}) for deriving from the second signal component estimate (ŝ_{MRC,n} ^{i}) a symbol estimate ({circumflex over (b)}_{MRC,n} ^{i}) and supplying said symbol estimate ({circumflex over (b)}_{MRC,n} ^{i}) to the constraint matrix providing means (42P,43P); the constraint means ( 42P,43P) comprises constraints-set generating means (42P) for generating a plurality of constraints-set matrices ( _{n−1}) together characterizing the subspace of interference attributable to said first group of user station signals and the constraint matrix generating means (43P) comprises vector reshaping means (48A^{1}, . . . , 48A^{N} ^{ c }) for reshaping the constraints-set matrices ( _{n} ^{1}, . . . , _{n} ^{N} ^{ c }) to form columns, respectively, of the constraint matrix (Ĉ_{n−1}), the constraint matrix generating means (43P) supplying the constraint matrix to each of said coefficient tuning means (50P^{d}) of said one or more receiver modules (20 ^{d}). 20. A receiver according to 30P^{i}) for providing amplitude estimates ({circumflex over (ψ)}_{n−1} ^{i}) of signal components from said first group of user stations and supplying the amplitude estimates to the constraints-set generating means (42P) as parts of the channel estimates ( _{n−1} ^{i}). 21. A receiver according to 1, #2, . . . , #N_{i}) to derive each symbol estimate, the arrangement being such that:
in each of the iterations of a particular frame (n), the constraints-set generator ( 42P) uses the channel estimates ( _{n−1} ^{i}), a first symbol estimate ({circumflex over (b)}_{n−2} ^{i}) and a second symbol estimate ({circumflex over (b)}_{MRC,n} ^{i}) from the first and second beamformer means, respectively of said one or more receiver modules, in a first iteration, the constraint matrix providing means ( 42P,43P) generates a first-iteration constraint matrix (Ĉ_{n−1}(1)) in dependence also upon a previous symbol estimate ({circumflex over (b)}_{MRC,n−1} ^{i}) previously generated by each of said second beamformers of said one or more receiver modules and supplies said first-iteration constraint matrix (Ĉ_{n−1}(1)) to the coefficient tuning means of the first beamformer means (47P^{i}) for use, with the spread channel estimate Ŷ _{0,n−1} ^{i}, to tune the coefficients of the first beamformer means (47P^{i}) for weighting each element of the delayed first observation vector (Y _{n−1}) to produce a first-iteration signal component estimate and the decision rule unit (29P/1 ^{i}) processes said first-iteration signal component estimate to produce a first-iteration symbol estimate ({circumflex over (b)}_{n−1} ^{i}(1)), in a second iteration, the constraint matrix generating means ( 43P) uses said first-iteration symbol estimate ({circumflex over (b)}_{n−1} ^{i}(1)), instead of the previous symbol estimate ({circumflex over (b)}_{MRC,n−1} ^{i}) to tune the weighting coefficients and derive a second-iteration constraint matrix (Ĉ_{n−1}(2)) for use by the first beamformer means and first decision rule means to produce a second-iteration symbol estimate ({circumflex over (b)}_{n−1} ^{i}(2)), and in a final iteration of a total of N _{s }iterations, the constraint matrix providing means (42P,43P) uses a penultimate-iteration symbol estimate ({circumflex over (b)}_{n−1} ^{i}(N_{s}−1)) produced by the first decision rule means (29P/1 ^{i}) in the penultimate iteration to provide a final-iteration constraint matrix (Ĉ_{n−1}(N_{s})) for use by the first combining means and first decision rule means to provide a final iteration symbol estimate ({circumflex over (b)}_{n−1} ^{i}(N_{s})) as the target symbol estimate of that frame (u) for output as the symbol estimate ({circumflex over (b)}_{n−1} ^{i}), and wherein the constraints-set generator (42P) buffers the symbol estimate ({circumflex over (b)}_{n−1} ^{i}) for use in every iteration of the next frame (n+1) instead of symbol estimate {circumflex over (b)}_{n−2} ^{i}, and the constraints-set generator (42P) uses a new symbol estimate ({circumflex over (b)}_{MRC,n+1} ^{i}) from said second beamformer (27P^{i}) for all iterations of the new frame, and uses the previous symbol estimate ({circumflex over (b)}_{MRC,n} ^{i}) from said second beamformer means (27P^{i}) in only the first iteration of said new frame, said previous symbol estimate being buffered as required and other variables being incremented appropriately. 22. A receiver according to 20 ^{1}, . . . , 20 ^{NI}, 20 ^{d}) comprises a first set (I) of receiver modules (20 ^{1}, . . . , 20 ^{NI}) for relatively strong user signals and that contribute at least respective sets of said channel estimates to said constraints-set generator (42) for use in deriving said constraint matrices but do not use said constraint matrices to update the weighting coefficients of their respective beamformer means, and a second set (D) of receiver modules (20 ^{d}) for relatively weaker user signals and that use the constraint matrices to update the weighting coefficients of their respective beamformer means but do not contribute either channel estimates or symbol estimates to the constraints-set generator (42) for use in deriving said constraint matrices. 23. A receiver according to 1) of at least one receiver module (20 ^{i}) as defined in 42P,43P) for use by receiver modules in other sets in deriving said constraint matrices and uses said constraint matrices derived from constraints supplied by receiver modules in its own set in tuning weighting coefficients of its beamformer means (47P^{i}). 24. A receiver according to 2) of at least one receiver module (20 ^{i}) as defined in 42P,43P) for use in deriving said constraint matrices and, in tuning weighting coefficients of its beamformer means (47P^{i}), uses said constraint matrices derived from constraints supplied by receiver modules in either or both of its own set and other sets. 25. A receiver according to 26. A receiver according to 27. A receiver according to 28. A receiver according to 29. A receiver according to 100 ^{d}) for multiplying a projection (Π_{n} ^{d}) with the observation vector (Y _{n}) to form an interference-reduced observation vector (Y _{n} ^{Π,d}), and a residual beamformer (27Q^{d}) responsive to the projection (Π_{n} ^{d}) and to the channel vector estimate (Ŷ _{0,n} ^{d}) to produce said signal component estimate (ŝ_{n} ^{d}), and the channel identification means (28Q^{d}) derives the channel vector estimate (Ŷ _{0,n} ^{d}) from the interference-reduced observation vector (Y _{n} ^{Π,d}). 30. A receiver according to 102Q^{d}) for reshaping the interference-reduced observation vector (Y _{n} ^{Π,d}) to form an interference-reduced observation matrix (Y_{n} ^{Π,d}), and a despreader (19 ^{d}) for despreading the interference-reduced observation matrix (Y_{n} ^{Π,d}), with the corresponding user spreading code to form a post-correlation reduced-interference observation vector (Z _{n} ^{Π,d}) for use by the channel identification means (28Qd) in deriving said channel vector estimate. 31. A receiver according to 20 ^{d}), said beamformer means (47R^{d,1}, . . . , 47R^{d,N} ^{ m }) uses different sets of weighting coefficients to weight each element of said observation vector (Y _{n}) to form a plurality of signal component estimates (ŝ_{n} ^{d,1}, . . . , ŝ_{n} ^{d,N} ^{ m }) corresponding to said respective ones of said series of symbols and the symbol estimating means derives from the plurality of signal component estimates (ŝ_{n} ^{d,1}, . . . , ŝ_{n} ^{d,N} ^{ m }) a corresponding plurality of symbol estimates ({circumflex over (b)}_{n} ^{d,1}, . . . , {circumflex over (b)}_{n} ^{d,N} ^{ m }), said observation vector deriving means comprises means (19 ^{d,1}, . . . , 19 ^{d,N} ^{ m }) for deriving from the observation matrix a plurality of post-correlation observation vectors (Z _{n} ^{d,1}, . . . , Z _{n} ^{d,N} ^{ m }) each corresponding to a respective one of the plurality of different spreading codes, the channel identification means (28R^{d}) derives from said plurality of post-correlation observation vectors (Z _{n} ^{d,1}, . . . , Z _{n} ^{d,N} ^{ m }) a corresponding plurality of sets of channel vector estimates (Ŷ _{0,n}, . . . , Ŷ _{0,n} ^{d,N} ^{ m }) and supplies the sets to said beamformer means (47R^{d,1}, . . . , 47R^{d,N} ^{ m }) and the coefficient tuning means of the beamformer means (47R^{d,1}, . . . , 47R^{d,N} ^{ m }) uses the sets of channel vector estimates (Ŷ _{0,n}, . . . , Ŷ _{0,n} ^{d,N} ^{ m }) to derive said different sets of weighting coefficients, respectively. 32. A receiver according to 19 ^{d,1}, . . . , 19 ^{d,N} ^{ m }) for deriving the plurality of observation vectors comprises despreading means for despreading said observation matrix (Y_{n}) using one or more of said plurality of different spreading codes to form a plurality of post-correlation observation vectors (Z _{n} ^{d,1}, . . . , Z _{n} ^{d,N} ^{ m }) for use by the channel identification means (28R^{d}) in deriving said plurality of spread channel vector estimates (Ŷ _{0,n} ^{d,1}, . . . , Ŷ _{0,n} ^{d,N} ^{ m }). 33. A receiver according to Z _{n} ^{d,1}, . . . , Z _{n} ^{d,N} ^{ m }) to said beamformer means (47R^{d,1}, . . . , 47R^{d,N} ^{ m }), and the coefficient tuning means therein uses said sets of weighting coefficients to weight elements of respective ones of the plurality of post-correlation observation vectors, and each of the user-specific constraint-matrix generator means comprises despreading means (55B^{d,1}, . . . , 55B^{d,N} ^{ m }) for despreading the user-specific constraints-set matrices using one or more of the plurality of different spreading codes. 34. A receiver according to 19 ^{d,δ}) for weighting said plurality of different spreading codes by the plurality of symbol estimates ({circumflex over (b)}_{n} ^{d,1}, . . . , {circumflex over (b)}_{n} ^{d,N} ^{ m }), respectively, to form a single spreading code and despreading the observation matrix (Y_{n}) using said single spreading code to produce a compound post-correlation observation vector (Z _{n} ^{d,δ}) for use by the channel identification means (28R^{d}) to derive said plurality of sets of channel vector estimates (Ŷ _{0,n}, . . . , Ŷ _{0,n} ^{d,N} ^{ m }). 35. A receiver according to Z _{n} ^{d,1}, . . . , Z _{n} ^{d,N} ^{ m }) to said beamformer means (47R^{d,1}, . . . , 47R^{d,N} ^{ m }), and the coefficient tuning means therein uses said sets of weighting coefficients to weight elements of respective ones of the plurality of post-correlation observation vectors, and each of the user-specific constraint-matrix generator means comprises despreading means (55B^{d,1}, . . . , 55B^{d,N} ^{ m }) for despreading the user-specific constraints-set matrices using the plurality of different spreading codes. 36. A receiver according to 20 ^{1}, . . . , 20 ^{NI}, 20 ^{d}) operates with a frame duration equal to integer multiples (F_{1}, . . . , F_{NI}, F_{d}) of symbol periods of the corresponding users and uses a plurality (F_{1}, . . . , F_{NI}, F_{d}) of different segments of the same long spreading code equal to said the number of symbol periods in said frame, in said at least one of the one or more receiver modules (20 ^{d}), said beamformer means (47S^{d,1}, . . . , 47S^{d,F} ^{ d }) uses different sets of weighting coefficients to weight each element of said observation vector (Y _{n}) to form a plurality (F_{d}) of signal component estimates (ŝ_{n} ^{d,1}, . . . , ŝ_{n} ^{d,F} ^{ d }), respectively, and the symbol estimating means (29S^{d,1}, . . . , 29S^{d,F} ^{ d }) derives from the plurality of signal component estimates (ŝ_{n} ^{d,1}, . . . , ŝ_{n} ^{d,F} ^{ d }) a corresponding plurality of symbol estimates ({circumflex over (b)}_{n} ^{d,1}, . . . , {circumflex over (b)}_{n} ^{d,F} ^{ d }), said observation vector deriving means deriving from the observation matrix one or more of a plurality of post-correlation observation vectors (Z _{n} ^{d,1}, . . . , Z _{n} ^{d,F} ^{ d }) the channel identification means (28S^{d}) derives from said post-correlation observation vectors (Z _{n} ^{d,1}, . . . , Z _{n} ^{d,F} ^{ d }) a corresponding plurality of spread channel vector estimates (Ŷ _{0,n} ^{d,1}, . . . , {circumflex over (Y)}_{0,n} ^{d,F} ^{ d }), and supplies said spread channel vector estimates (Ŷ _{0,n} ^{d,1}, . . . , Ŷ _{0,n} ^{d,F} ^{ d }), to said beamformer means (47S^{d,1}, . . . , 47S^{d,F} ^{ d }), each spread channel vector estimate being spread by a respective one of the segments of the long spreading code, and the coefficient tuning means of the beamformer means (47S^{d,1}, . . . , 47S^{d,F} ^{ d }) uses said channel vector estimates (Ŷ _{0,n} ^{d,1}, . . . , Ŷ _{0,n} ^{d,F} ^{ d }) to derive said different sets of weighting coefficients. 37. A receiver according to 19S^{d,1}, . . . , 19S^{d,F} ^{ d }) for deriving one or more of the plurality of post-correlation observation vectors comprises despreading means for despreading said observation matrix (Y_{n}) using one or more of said different segments of the same long spreading code such that said plurality of observation vectors comprise a plurality of post-correlation observation vectors (Z _{n} ^{d,1}, . . . , Z _{n} ^{d,F} ^{ d }) one or more thereof for use by the channel identification means (28S^{d}). 38. A receiver according to Z _{n} ^{d,1}, . . . , Z _{n} ^{d,F} ^{ d }) to said beamformer means (47S^{d,1}, . . . , 47S^{d,F} ^{ d }), and the coefficient tuning means therein uses said sets of weighting coefficients to weight elements of respective ones of the plurality of post-correlation observation vectors, and each of the user-specific constraint-matrix generator means comprises despreading means (55B^{d,1}, . . . , 55B^{d,F} ^{ d }) for despreading the user-specific constraints-set matrices using the corresponding one of the plurality (F_{d}) of different spreading code segments. 39. A user station receiver for a CDMA communications system comprising a plurality (NB) of base stations (11) and a multiplicity (U) of user stations (10 ^{1}, . . . , 10 ^{U}), at least a plurality (U′) of the user stations being in a cell associated with one of said base stations and served thereby, said one base station having a plurality of transmitter modules for spreading user signals for transmission to the plurality (U′) of user stations, respectively, and a receiver for receiving spread user signals transmitted by the plurality (U′) of user stations, the user stations each having a receiver for receiving the corresponding spread user signal transmitted by the base station, said plurality (U′) of user stations each having a unique spreading code assigned thereto for use by the user station and the corresponding one of the base station transmitter modules to spread the user signals of that user for transmission,
the spread user signals transmitted from the base station transmitter modules to a particular one of the plurality (U′) of user stations propagating via a plurality of channels ( 14 ^{1}, . . . , 14 ^{U′}), respectively, the receiver of a particular one of said plurality (U′) of user stations receiving a signal (X(t)) comprising components corresponding to spread user signals for said particular user station and spread user signals transmitted by other transmitter modules of said plurality (NB) of base stations for other users, each of said spread user signals comprising a series of symbols spread using the spreading code associated with the corresponding one of the user stations, said user station receiver comprising:
a plurality (NB) of receiver modules (
20 ^{ν′}) each for deriving from successive frames of the received signal (X(t)) estimates of sets of said series of symbols from a corresponding one of the base stations, preprocessing means (
18) for deriving from the received signal (X(t)) a series of observation matrices (Y_{n}) each for use by each of the receiver modules (20 ^{ν′}) in a said frame to derive estimates of sets of said symbols, and means (
19,44) for deriving from each observation matrix a plurality of sets of observation vectors (Y _{n} ^{ν′,1,1}, . . . , Y _{n} ^{ν′,NI,F} ^{ NI }; Z _{n} ^{ν′,1,1}, . . . , Z _{n} ^{ν′,NI,F} ^{ NI }) and applying each of the sets of observation vectors to a respective one of the plurality of receiver modules (20 ^{ν′}); each receiver modules comprising;
channel identification means (
28T^{ν′}) for deriving from the respective one of the sets of observation vectors a set of spread channel vector estimates (Ŷ _{0,n} ^{ν′,1,1}, . . . , Ŷ _{0,n} ^{ν′,NI,F} ^{ NI }) based upon parameter estimates of the channel between the corresponding one of the base stations and said user station; beamformer means (
47T^{ν′,1,1}, . . . , 47T^{ν′,NI,F} ^{ NI }) having coefficient tuning means for producing sets of weighting coefficients in dependence upon the sets of channel vector estimates, respectively, and combining means for using each of the sets of weighting coefficients to weight respective ones of the elements of a respective one of the observation vectors and combining the weighted elements to provide a corresponding set of signal component estimates (ŝ_{n} ^{ν′,1,1}, . . . , ŝ_{n} ^{ν′,NI,F} ^{ NI }) and symbol estimating means (
29T^{ν′,1,1}, . . . , 29T^{ν′,NI,F} ^{ NI }) for deriving from the set of signal component estimates a set of estimates ({circumflex over (b)}_{n} ^{ν′,1,1}, . . . , {circumflex over (b)}_{n} ^{ν′,NI,F} ^{ NI }) of symbols spread by the corresponding one of the transmitter modules and transmitted by the base station; said user station receiver further comprising means (
42,43) responsive to said symbol estimates ({circumflex over (b)}_{n} ^{ν′,1,1}, . . . , {circumflex over (b)}_{n} ^{ν′,NI,F} ^{ NI }; g_{n} ^{1}, g_{n} ^{2}, g_{n} ^{3}) and channel estimates ( _{n} ^{ν′}) from each of said plurality (NB) of receiver modules, said channel estimates comprising at least channel vector estimates (Ĥ _{n} ^{ν′}) for channels (14 ^{ν′}) between the user station receiver and said base stations, for providing at least one constraint matrix (Ĉ_{n}) representing interference subspace of components of the received signal corresponding to said spread signals, and in each of said receiver modules (20 ^{ν′}), the coefficient tuning means produces said sets of weighting coefficients in dependence upon both the constraint matrix (Ĉ_{n}) and the channel vector estimates so as to tune said receiver module (20 ^{ν′}) towards a substantially null response to that portion of the received signal (X(t)) corresponding to said interference subspace. 40. A user station receiver according to 19 ^{ν′,1,1}, . . . , 19 ^{ν′,NI,F} ^{ NI }) for deriving from the observation matrix a plurality of post-correlation observation vectors (Z _{n} ^{ν′,1,1}, . . . , Z _{n} ^{ν′,NI,F} ^{ NI }) and supplying said plurality of post-correlation observation vectors (Z _{n} ^{ν′,1,1}, . . . , Z _{n} ^{ν′,NI,F} ^{ NI }) to the channel identification means (28T^{ν′}) for use in producing said sets of channel vector estimates (Ŷ _{0,n} ^{ν′,1,1}, . . . , Ŷ _{0,n} ^{ν′,NI,F} ^{ NI }). 41. A user station receiver according to 20 ^{ν′}) derive symbols for user signals other than those destined for the user of said user station receiver, and said user station receiver comprises an additional receiver module (20 ^{d}) for deriving symbols from the received signal destined for said user of said user station receiver and transmitted by a corresponding serving one (ν) of said plurality of base stations, wherein each of said plurality of receiver modules (20 ^{ν′}, 20 ^{d}) operates with a frame duration equal to an integer multiple of the symbol period and uses a corresponding number of segments (F_{1}, . . . , F_{NI}, F_{d}) of a long spreading code, each segment corresponding to a respective one of a plurality (F_{1}, . . . , F_{NI}, F_{d}) of different segments of the same long spreading code equal to said plurality of symbol periods in said frame, and wherein, in said additional receiver module (20 ^{d}), said beamformer means (47S^{d,1}, . . . , 47S^{d,F} ^{ d }) uses different sets of weighting coefficients to weight each element of said observation vector (Y _{n}) to form a plurality (F_{d}) of signal component estimates (ŝ_{n} ^{d,1}, . . . , ŝ_{n} ^{d,F} ^{ d }), respectively, and the symbol estimating means (29S^{d,1}, . . . , 29S^{d,F} ^{ d }) derives from the plurality of signal component estimates (ŝ_{n} ^{d,1}, . . . , ŝ_{n} ^{d,F} ^{ d }) a corresponding plurality of symbol estimates ({circumflex over (b)}_{n} ^{d,1}, . . . , {circumflex over (b)}_{n} ^{d,F} ^{ d }), said observation vector deriving means deriving from the observation matrix a plurality of observation vectors (Y _{n} ^{d,1}, . . . , Y _{n} ^{d,F} ^{ d }) the channel identification means (28S^{d}) derives from said observation vectors (Y _{n} ^{d,1}, . . . , Y _{n} ^{d,F} ^{ d }) a corresponding plurality of spread channel vector estimates (Ŷ _{0,n}, . . . , Ŷ _{0,n} ^{d,F} ^{ d }), and supplies same said beamformer means (47S^{d,1}, . . . , 47S^{d,F} ^{ d }), each spread channel vector estimate being spread by a respective one of the segments of the long spreading code, and the coefficient tuning means of the beamformer means (47S^{d,1}, . . . , 47S^{d,F} ^{ d }) uses said channel vector estimates (Ŷ _{0,n} ^{d,1}, . . . , Ŷ _{0,n} ^{d,F} ^{ d }) to derive said different sets of weighting coefficients. 42. A user station receiver according to 20 ^{ν′}) derive symbols for user signals other than those destined for the user of said user station receiver, and said user station receiver comprises an additional receiver module (20 ^{d}) for deriving symbols from the received signal destined for said user of said user station receiver, wherein each of said plurality of receiver modules (20 ^{ν′},20 ^{d}) operates with a frame duration equal to an integer multiple of the symbol period and uses a corresponding number of segments of a long spreading code, each segment corresponding to a respective one of a plurality (F_{1}, . . . , F_{NI}, F_{d}) of different segments of the same long spreading code equal to said plurality of symbol periods in said frame, and wherein, in said additional receiver module (20 ^{d}), said beamformer means (47T^{ν,d,1}, . . . , 47T^{ν,d,F} ^{ d }) uses different sets of weighting coefficients to weight each element of said observation vector (Y _{n}) to form a plurality (F_{d}) of signal component estimates (ŝ_{n} ^{ν,d,1}, . . . , ŝ_{n} ^{ν,d,F} ^{ d }), respectively, and the symbol estimating means (29T^{ν,d,1}, . . . , 29T^{ν,d,F} ^{ d }) derives from the plurality of signal component estimates (ŝ_{n} ^{ν,d,1}, . . . , ŝ_{n} ^{ν,d,F} ^{ d }) a corresponding plurality of symbol estimates ({circumflex over (b)}_{n} ^{ν,d,1}, . . . , {circumflex over (b)}_{n} ^{ν,d,F} ^{ d }), and wherein the coefficient tuning means of said beamformer means (47T^{ν,d,1}, . . . , 47T^{ν,d,F} ^{ d }) derives the weighting coefficients using said constraint matrix received from the constraint matrix generating means (43T) and said spread channel vector estimates (Ŷ _{0,n} ^{ν,d,1}, . . . , Ŷ _{0,n} ^{ν,d,F} ^{ d }) produced by the channel identification means (28T^{ν}) of the receiver module (20 ^{ν}) corresponding to the base station (ν) serving said user station. 43. A user station receiver according to 19 ^{ν′,1,1}, . . . , 19 ^{ν′,NI,F} ^{ NI }) for despreading the observation matrix to form a plurality of post-correlation observation vectors (Z _{n} ^{ν′,1,1}, . . . , Z _{n} ^{ν′,NI,F} ^{ NI }) and supplying said plurality of post-correlation observation vectors (Z _{n} ^{ν′,1,1}, . . . , Z _{n} ^{ν′,NI,F} ^{ NI }) to the channel identification means (28T^{ν′}) for use in producing said sets of channel vector estimates (Ŷ _{0,n} ^{ν′,1,1}, . . . , Ŷ _{0,n} ^{ν′,NI,F} ^{ NI }). 44. A user station receiver according to _{m}) of different spreading codes to spread respective ones of said series of symbols for simultaneous transmission in the same frame, such that the component of the received signal (X(t)) corresponding to that base station transmitter module comprises a corresponding plurality of spread signals, and at least one (20 ^{ν′}) of the plurality of receiver modules (20U) further comprises amplitude estimation means (30U^{ν′}) for deriving total amplitude of a set of signal component estimates (ŝ_{n} ^{ν′,1,1}, . . . , ŝ_{n} ^{ν′,NI,N} ^{ m }) produced by beamformer means (47U^{ν′,1,1}, . . . , 47U^{ν′,NI,N} ^{ m }) thereof, said beamformer means (47U^{ν′,1,1}, . . . , 47U^{ν′,NI,N} ^{ m }) uses different sets of weighting coefficients to weight each element of said observation vector (Y _{n}) to form said plurality of signal component estimates (ŝ_{n} ^{ν′,1,1}, . . . , ŝ_{n} ^{ν′,NI,N} ^{ m }) corresponding to said respective ones of said series of symbols, and the symbol estimating means (29U^{ν′,1,1}, . . . , 29U^{ν′,NI,N} ^{ m }) derives from the plurality of signal component estimates (ŝ_{n} ^{ν′,1,1}, . . . , ŝ_{n} ^{ν′,NI,N} ^{ m }) a corresponding plurality of symbol estimates ({circumflex over (b)}_{n} ^{ν′,1,1}, . . . , {circumflex over (b)}_{n} ^{ν′,NI,N} ^{ m }), the channel identification means (28U^{ν′}) derives a corresponding plurality of sets of spread channel vector estimates (Ŷ _{0,n} ^{ν′,1,1}, . . . , Ŷ _{0,n} ^{ν′,NI,N} ^{ m }) each spread by a respective one of said plurality of different spreading codes, and supplies the sets to said beamformer means (47U^{ν′,1,1}, . . . , 47U^{ν′,NI,N} ^{ m }) and the coefficient tuning means of the beamformer means (47U^{ν′,1,1}, . . . , 47U^{ν′,NI,N} ^{ m }) uses the sets of spread channel vector estimates (Ŷ _{0,n} ^{ν′,1,1}, . . . , Ŷ _{0,n} ^{ν′,NI,N} ^{ m }) to derive said different sets of weighting coefficients, respectively. 45. A user station receiver according to 20 ^{ν′}) derive symbols for user signals other than those destined for the user of said user station receiver, and said user station receiver comprises an additional receiver module (20 ^{d}) for deriving symbols from the received signal destined for said user of said user station receiver, wherein each of said plurality of receiver modules (20 ^{ν′},20 ^{d}) operates with a frame duration equal to an integer multiple of the symbol period and uses a corresponding number of segments of a long spreading code, each segment corresponding to a respective one of a plurality (N_{m}, F_{d}) of different segments of the same long spreading code equal to said plurality of symbol periods in said frame, and wherein, in said additional receiver module (20 ^{d}), said beamformer means (47T^{ν,d,1}, . . . , 47T^{ν,d,F} ^{ d }) uses different sets of weighting coefficients to weight each element of said observation vector (Y _{n}) to form a plurality (F_{d}) of signal component estimates (ŝ_{n} ^{ν,d,1}, . . . , ŝ_{n} ^{ν,d,F} ^{ d }), respectively, and the symbol estimating means (29T^{ν,d,1}, . . . , 29T^{ν,d,F} ^{ d }) derives from the plurality of signal component estimates (ŝ_{n} ^{ν,d,1}, . . . , ŝ_{n} ^{ν,d,F} ^{ d }) a corresponding plurality of symbol estimates ({circumflex over (b)}_{n} ^{ν,d,1}, . . . , {circumflex over (b)}_{n} ^{ν,d,F} ^{ d }), and wherein the coefficient tuning means of said beamformer means (47T^{ν,d,1}, . . . , 47T^{ν,d,F} ^{ d }) derives the weighting coefficients using said constraint matrix received from the constraint matrix generating means (43T) and said spread channel vector estimates (Ŷ _{0,n} ^{ν,d,1}, . . . , Ŷ _{0,n} ^{ν,d,F} ^{ d }) produced by the channel identification means (28T^{ν}) of the receiver module (20 ^{ν}) corresponding to the base station (ν) serving said user station. 46. A receiver according to 19 ^{ν′,1,1}, . . . , 19 ^{ν′,NI,N} ^{ m }) for despreading the observation matrix to form a plurality of post-correlation observation vectors (Z _{n} ^{ν′,1,1}, . . . , Z _{n} ^{ν′,NI,N} ^{ m }) and supplying said plurality of post-correlation observation vectors (Z _{n} ^{ν′,1,1}, . . . , Z _{n} ^{ν′,NI,N} ^{ m }) to the channel identification means (28U^{ν′}) for use in producing said sets of channel vector estimates (Ŷ _{0,n} ^{ν′,1,1}, . . . , Ŷ _{0,n} ^{ν′,NI,N} ^{ m }).Description [0001] 1. Technical Field [0002] The invention relates to Code-Division Multiple Access (CDMA) communications systems, which may be terrestrial or satellite systems, and in particular to interference suppression in CDMA communications systems. [0003] 2. Background Art [0004] Code-Division Multiple Access communications systems are well known. For a general discussion of such systems, the reader is directed to a paper entitled “Multiuser Detection for CDMA Systems” by Duel-Hallen, Holtzman and Zvonar, [0005] In CDMA systems, the signals from different users all use the same bandwidth, so each user's signal constitutes noise or interference for the other users. On the uplink (transmissions from the mobiles) the interference is mainly that from other transmitting mobiles. Power control attempts to maintain the received powers at values that balance the interference observed by the various mobiles, but, in many cases, cannot deal satisfactorily with excessive interference. Where mobiles with different transmission rates are supported within the same cells, the high-rate mobiles manifest strong interference to the low-rate mobiles. On the downlink (transmission towards the mobiles) transmissions from base-stations of other cells as well as strong interference from the same base-station to other mobiles may result in strong interference to the intended signal. Downlink power control may be imprecise or absent altogether. In all these so called near-far problem cases, the transmission quality can be improved, or the transmitted power reduced, by reducing the interference. In turn, for the same transmission quality, the number of calls supported within the cell may be increased, resulting in improved spectrum utilization. [0006] Power control is presently used to minimize the near-far problem, but with limited success. It requires a large number of power control updates, typically 800 times per second, to reduce the power mismatch between the lower-rate and higher-rate users. It is desirable to reduce the number of communications involved in such power control systems, since they constitute overhead and reduce overall transmission efficiencies. Nevertheless, it is expected that future CDMA applications will require even tighter power control with twice the number of updates, yet the near-far problem will not be completely eliminated. It is preferable to improve the interference suppression without increasing the number of transmissions by the power control system. [0007] Multiuser detectors achieve interference suppression to provide potential benefits to CDMA systems such as improvement in capacity and reduced precision requirements for power control. However, none of these detectors is cost-effective to build with significant enough performance advantage over present day systems. For example, the complexity of the optimal maximum likelihood sequence detector (MLSD) is exponential in the number of interfering signals to be cancelled, which makes its implementation excessively complex. Alternative suboptimal detectors fall into two groups: linear and subtractive. The linear detectors include decorrelators, as disclosed by K. S. Schneider, “Optimum detection of code division multiplexed signals”, [0008] Z. Xie, R. T. Short, and C. K. Rushforth, “A family of suboptimum detectors for coherent multiuser communications”, [0009] Subtractive interference cancellation detectors take the form of successive interference cancellers (SIC), as disclosed by R. Kohno et al., “Combination of an adaptive array antenna and a canceller of interference for direct-sequence spread-spectrum multiple-access system”, [0010] One particular subtractive technique was disclosed by Shimon Moshavi in a paper entitled “Multi-User Detection for DS-CDMA Communications”, [0011] A disadvantage of this approach is its sensitivity to the data and power estimates, i.e., their accuracy and the sign of the data. A wrong decision will result in the interference component being added rather than subtracted, which will have totally the wrong effect. [0012] For more information about these techniques, the reader is directed to a paper by P. Patel and J. Holtzman entitled “Analysis of a Simple Successive Interference Cancellation Scheme in a DS/CDMA System”, [0013] In a paper entitled “A New Receiver Structure for Asynchronous CDMA: STAR—The Spatio-Temporal Array-Receiver”, [0014] The present invention addresses the need for improved interference suppression without the number of transmissions by the power control system being increased, and, to this end, provides a receiver for a CDMA communications system which employs interference subspace rejection to obtain a substantially null response to interference components from selected user stations. Preferably, the receiver also provides a substantially unity response for a propagation channel via which a corresponding user's “desired” signal was received. [0015] According to one aspect of the invention, there is provided a receiver suitable for a base station of a CDMA communications system comprising at least one base station ( [0016] a plurality (U′) of receiver modules ( [0017] preprocessing means ( [0018] means ( [0019] channel identification means ( [0020] beamformer means ( [0021] symbol estimating means ( [0022] wherein said receiver further comprises means ( [0023] and to channel estimates ( _{n} ^{1 }. . . _{n} ^{NI}; _{n−1} ^{i}) comprising at least said channel vector estimates (Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI}) for channels (14 ^{1}, . . . , 14 ^{NI}) of a first group (I) of said plurality of user stations (10 ^{1}, . . . , 10 ^{NI}) to provide at least one constraint matrix (Ĉ_{n}) representing interference subspace of components of the received signal corresponding to said predetermined group, and in each of one or more receiver modules (20A^{d}) of a second group (D) of said plurality of receiver modules, the coefficient tuning means (50A^{d}) produces said set of weighting coefficients in dependence upon both the constraint matrix (Ĉ_{n}) and the channel vector estimates (Ĥ _{n} ^{d}) so as to tune said one or more receiver modules (20A^{d}) each towards a substantially null response to that portion of the received signal (X(t)) corresponding to said interference subspace.
[0024] Embodiments of the invention may employ one of several alternative modes of implementing interference subspace rejection (ISR), i.e. characterizing the interference and building the constraint matrix. In a first embodiment, using a first mode conveniently designated ISR-TR, each receiver module in the first group generates its re-spread signal taking into account the amplitude and sign of the symbol and the channel characteristics. The re-spread signals from all of the receiver modules of the first group are summed to produce a total realization which is supplied to all of the receiver modules in the second group. [0025] Where each receiver module of the second set uses decision feedback, it further comprises delay means for delaying each frame/block of the observation vector before its application to the beamformer. [0026] Whereas, in ISR-TR embodiments, just one null constraint is dedicated to the sum, in a second embodiment, which uses a second mode conveniently designated ISR-R, estimated realisations of all the interferers are used, and a null constraint is dedicated to each interference vector. In this second embodiment, in each receiver module of the first set, the symbols spread by the spreader comprise estimated realisations of the symbols of the output signal. Also, the constraint waveforms are not summed before forming the constraint matrix. Thus, the receiver module estimates separately the contribution to the interference from each unwanted (interfering) user and cancels it by a dedicated null-constraint in the multi-source spatio-temporal beamformer. In most cases, estimation of the interference requires estimates of the past, present and future data symbols transmitted from the interferers, in which case the receiver requires a maximum delay of one symbol and one processing cycle for the lower-rate or low-power users and, at most a single null constraint per interferer. [0027] In a third embodiment of the invention which uses a third mode conveniently designated ISR-D, i.e. the observation vector/matrix is decomposed over sub-channels/fingers of propagation path and the beamformer nulls interference in each of the sub-channels, one at a time. In most cases, the maximum number of constraints per interferer is equal to the number of sub-channels, i.e. the number of antenna elements M multiplied by the number of paths P. [0028] In a fourth embodiment using a fourth mode conveniently designated ISR-H because it implements null-responses in beamforming using hypothetical realisations of the interference, without any delay, each receiver module of the first group further comprises means for supplying to the spreader possible values of the instant symbols of the output signal and the spreader supplies a corresponding plurality of re-spread signals to each of the receiver modules of the second group. In each receiver module of the second group, the despreader despreads the plurality of re-spread signals and supplies corresponding despread vectors to the beamformer. This embodiment suppresses any sensitivity to data estimation errors and, in most cases, requires a maximum of 3 null constraints per interferer. [0029] In a fifth embodiment using a fifth mode conveniently designated ISR-RH because it uses the past and present interference symbol estimates, in each receiver module of the first group, the spreader spreads the symbols of the output signal itself and, in each receiver module of the second group, the beamformer then implements null-responses over reduced possibilities/hypotheses of the interference realization. Conveniently, application of the output of the first despreader to the beamformer will take into account the time required for estimation of the interferer's symbol. In most cases, the beamformer will provide a maximum of 2 null constraints per interferer. [0030] In any of the foregoing embodiments of the invention, the channel identification unit may generate the set of channel vector estimates in dependence upon the extracted despread data vectors and the user signal component estimate. [0031] For each of the above-identified modes, the receiver modules may employ either of two procedures. On the one hand, the receiver module may apply the post-correlation observation vector to the channel identification unit but supply the observation matrix itself directly to the beamformer, i.e. without despreading it. The constraint matrix then would be supplied to the beamformer without despreading. [0032] Alternatively, each receiver module could supply the post-correlation observation vector to both the channel identification unit and the beamformer. In this case, the receiver module would also despread the constraint matrix before applying it to the beamformer. [0033] Where the reception antenna comprises a plurality of antenna elements, the beamformer unit may comprise a spatio-temporal processor, such as a filter which has coefficients tuned by the estimated interference signals. [0034] The receiver modules may comprise a first set that are capable of contributing a constraint waveform to the constraint matrix and a second set that have a beamformer capable of using the constraint matrix to tune the specified null response and unity response. In preferred embodiments, at least some of the plurality of receiver modules are members of both the first set and the second set, i.e. they each have means for contributing a constraint waveform and a beamformer capable of using the constraint matrix. [0035] In practice, the receiver modules assigned to the stronger user signals will usually contribute a constraint waveform and the beamformer units of the receiver modules assigned to other user signals will be capable of using it. [0036] The receiver module may comprise an MRC beamformer and an ISR beamformer and be adapted to operate in multi-stage, i.e., for each symbol period of frame, it will carry out a plurality of iterations. In the first iteration, the constraints set generator will receive the “past” and “future” estimates from the MRC beamformer and the “past” symbol estimate, i.e., from the previous frame, and process them to produce a new symbol estimate for the first iteration. In subsequent iterations of the current symbol period or frame, the constraints-set generator will use the “future” estimate from the MRC beamformer, the previous estimate from the ISR beamformer and the symbol estimate generated in the previous iteration. The cycle will repeat until the total number of iterations have been performed, whereupon the output from the receiver module is the desired estimated symbol for the current frame which then is used in the similar iterations of the next frame. [0037] The ISR receiver module comprising both an MRC beamformer and an ISR beamformer may comprise means { [0038] The ISR beamformer may process blocks or frames of the observation vector that are extended by concatenating a current set of data with one or more previous frames or blocks of data. [0039] The different receiver modules may use different sizes of frame. [0040] In order to receive signals from a user transmitting multicode signals, the ISR receiver module may comprise a plurality of ISR beamformers and despreaders, each for operating upon a corresponding one of the multiple codes. The channel identification unit then will produce a channel vector estimate common to all of the multicodes, spread that channel vector estimate with each of the different multicodes and supply the resulting plurality of spread channel vector estimates to respective ones of the plurality of ISR beamformers. [0041] The channel identification unit of the multicode ISR receiver module may receive its post-correlation observation vector from a despreader ( [0042] The ISR receiver module may comprise a despreader [0043] Embodiments of the invention may be adapted for use in a user/mobile station capable of receiving user-bound signals transmitted by a plurality of base stations each to a corresponding plurality of users, the receiver then comprising a selection of receiver modules each corresponding to a different base station and configured to extract a preselected number of said user-bound signals. Where the particular user/mobile station is included in the preselected number, the receiver module may comprise a similar structure to the above-mentioned multicode receiver, the plurality of despreaders being adapted to despread the observation matrix using respective ones of a set of codes determined as follows: (1) a pre-selected number NB of base stations from which the mobile receives signals and which have been selected for cancellation—represented by index ν′ which ranges from 1 to NB; (2) a preselected number (1 to NI) of interferers per base station preselected for cancellation; (3) the data rates of the selected interferers. [0044] Thus, according to a second aspect of the invention, there is provided a user station receiver for a CDMA communications system comprising a plurality (NB) of base stations ( [0045] the spread user signals transmitted from the base station transmitter modules to a particular one of the plurality (U′) of user stations propagating via a plurality of channels ( [0046] the receiver of a particular one of said plurality (U′) of user stations receiving a signal (X(t)) comprising components corresponding to spread user signals for said particular user station and spread user signals transmitted by other transmitter modules of said plurality (NB) of base stations for other users, each of said spread user signals comprising a series of symbols spread using the spreading code associated with the corresponding one of the user stations, [0047] said user station receiver comprising: [0048] a plurality (NB) of receiver modules ( [0049] preprocessing means ( [0050] means ( [0051] each receiver modules comprising; [0052] channel identification means ( [0053] beamformer means ( [0054] symbol estimating means ( [0055] said user station receiver further comprising means ( _{n} ^{ν′}) from each of said plurality (NB) of receiver modules, said channel estimates comprising at least channel vector estimates (Ĥ _{n} ^{ν′}) for channels (14 ^{ν′}) between the user station receiver and said base stations, for providing at least one constraint matrix (Ĉ_{n}) representing interference subspace of components of the received signal corresponding to said spread signals, and in each of said receiver modules (20 ^{ν′}), the coefficient tuning means produces said sets of weighting coefficients in dependence upon both the constraint matrix (Ĉ_{n}) and the channel vector estimates so as to tune said receiver module (20 ^{ν′}) towards a substantially null response to that portion of the received signal (X(t)) corresponding to said interference subspace.
[0056] Where the signal destined for the particular user/mobile station is not one of the preselected number of signals from the corresponding base station, the receiver may further comprise an ISR receiver module which has means for updating the ISR beamformer coefficients using the channel vector estimates from at least some of the receiver modules that have generated such channel vector estimates for the preselected signals for the same base station. [0057] Where the rates of the different users are not known to the instant mobile station, the codes may comprise a fixed number of segments N [0058] The complexity of the multicode embodiments may be reduced by reducing the number of codes that are used by the despreaders. In particular, the bank of despreaders may use a set of codes that represent summation of the codes of the different NI interferers, to form a compound code which reduces the total number of codes being used in the despreaders. [0059] According to another aspect of the invention, there is provided a STAR receiver comprising an MRC beamformer which operates upon an observation vector which has not been despread. [0060] Of course, that does not preclude having all channels feed their interference components to all other channels. [0061] Receivers embodying the present invention can operate in a multiple-input, multiple-output (MIMO) system, i.e. with multiple transmit antennas and multiple receive antennas. [0062] The foregoing and other objects, features, aspects and advantages of the present invention will become more apparent from the following detailed description, in conjunction with the accompanying drawings, of preferred embodiments of the invention. [0063]FIG. 1 is a schematic diagram illustrating a portion of a CDMA communications system comprising a plurality of user stations, typically mobile, and a base station having a reception antenna comprising an array of antenna elements, and illustrating multipath communication between one of the user stations and the array of antennas; [0064]FIG. 2 is a simplified schematic diagram representing a model of the part of the system illustrated in FIG. 1; [0065]FIG. 3 is a detail block diagram of a spreader portion of one of the user stations; [0066] FIGS. [0067]FIG. 5 is a simplified block schematic diagram of a base station receiver according to the prior art; [0068]FIG. 6 is a detail block diagram of a preprocessing unit of the receiver; [0069]FIG. 7 is a detail block diagram of a despreader of the receiver; [0070]FIG. 8 illustrates several sets of users in a CDMA system ranked according to data rate; [0071]FIG. 9 is a detail block diagram showing several modules of a receiver embodying the present invention, including one having a beamformer operating on data that has not been despread; [0072]FIG. 10 is a detail schematic diagram showing a common matrix generator and one of a plurality of beamformers coupled in common thereto; [0073]FIG. 11 is a block diagram corresponding to FIG. 9 but including a module having a beamformer operating upon data which has first been despread; [0074]FIG. 12 is a schematic diagram of a user-specific matrix generator and an associated beamformer of one of the receiver modules of FIG. 11; [0075]FIG. 13 is a detail block schematic diagram of a receiver using total realisation of the interference to be cancelled (ISR-TR) and without despreading of the data processed by the beamformer; [0076]FIG. 14 illustrates a respreader of one of the receiver modules of FIG. 13; [0077]FIG. 15 is a detail block schematic diagram of a receiver using individual realisations of the interference (ISR-R) and without despreading of the data processed by the beamformer; [0078]FIG. 16 is a simplified block diagram of a receiver which decomposes each realisation of the interference over diversity paths (ISR-D) and without despreading of the data processed by the beamformer; [0079]FIG. 17 is a simplified schematic block diagram of a receiver employing interference subspace rejection based upon hypothetical values of the symbols (ISR-H) and without despreading of the data processed by the beamformer; [0080]FIG. 18 illustrates all possible triplets for the hypothetical values; [0081]FIG. 19 illustrates bit sequences for generating the hypothetical values; [0082]FIG. 20 is a simplified schematic block diagram of a receiver employing interference subspace rejection based upon both hypothetical values of the symbols and realisations (ISR-RH) and without despreading of the data processed by the beamformer; [0083]FIG. 21 is a simplified schematic block diagram of a receiver similar to the ISR-TR receiver shown in FIG. 13 but in which the beamformer operates upon the data that has first been despread; [0084]FIG. 22 is a simplified schematic block diagram of a receiver similar to the ISR-R receiver shown in FIG. 15 but in which the beamformer operates upon data that has first been despread; [0085]FIG. 23 is a simplified schematic block diagram of a receiver similar to the ISR-D receiver shown in FIG. 16 but in which the beamformer operates upon data that has first been despread; [0086]FIG. 24 is a simplified schematic block diagram of a receiver similar to the ISR-H receiver shown in FIG. 18 but in which the beamformer operates upon data that has first been despread; [0087]FIG. 25 illustrates bit sequences generated in the receiver of FIG. 24; [0088]FIG. 26 is a simplified schematic block diagram of a receiver similar to the ISR-RH receiver shown in FIG. 20 but in which the beamformer operates upon data that has first been despread; [0089]FIG. 27 illustrates an alternative STAR module which may be used in the receiver of FIG. 5 or in place of some of the receiver modules in the receivers of FIGS. [0090]FIG. 28 illustrates a receiver module which both contributes to the constraint matrix and uses the constraint matrix to cancel interference (JOINT-ISR); [0091]FIG. 29 illustrates a multi-stage ISR receiver module; [0092]FIG. 30 illustrates successive implementation of ISR; [0093]FIG. 31 illustrates a receiver module which uses ISR to enhance channel identification; [0094]FIG. 32 illustrates extension of the frame size to reduce noise enhancement and facilitate asynchronous operation and processing of high data rates; [0095]FIG. 33 illustrates implementation of ISR with mixed spreading factors; [0096]FIG. 34 illustrates an uplink ISR receiver module for a user employing multicode signals; [0097]FIG. 35 illustrates a modification of the receiver module of FIG. 34; [0098]FIG. 36 illustrates how multirate can be modelled as multicode; [0099]FIG. 37 illustrates frame size determination for multirate signals; [0100]FIG. 38 illustrates grouping of multirate signals to correspond to a specific user's symbol rate; [0101]FIG. 39 illustrates an “uplink” multirate ISR receiver module for a base station; [0102]FIG. 40 illustrates one of a plurality of “downlink” multirate receiver modules for a user station operating as a “virtual base station”; [0103]FIG. 41 illustrates a “downlink” multirate receiver module of the user station of FIG. 41 for extracting signals for that user station; [0104]FIG. 42 illustrates a multicode alternative to the receiver module of FIG. 40; [0105]FIG. 43 illustrates a second alternative to the receiver module of FIG. 40; [0106]FIG. 44 illustrates an ISR receiver module using pilot symbols; [0107]FIG. 45 illustrates in more detail an ambiguity estimator of the receiver module of FIG. 44; [0108]FIG. 46 illustrates an alternative ISR receiver module using pilot channels; [0109]FIG. 47 illustrates an alternative ISR receiver module employing symbol decoding at an intermediate stage; [0110]FIG. 48 illustrates modelling of the downlink as an uplink; and [0111]FIG. 49 illustrates a transmitter having multiple antennas with which receivers embodying the invention can operate. [0112] In the following description, identical or similar items in the different Figures have the same reference numerals, in some cases with a suffix. [0113] The description refers to several published articles. For convenience, the articles are cited in full in a numbered list at the end of the description and cited by that number in the description itself. The contents of these articles are incorporated herein by reference and the reader is directed to them for reference. [0114]FIGS. 1 and 2 illustrate the uplink of a typical asynchronous cellular CDMA system wherein a plurality of mobile stations [0115] As before, it is presumed that the base station knows the spreading codes of all of the mobile stations with which it communicates. The mobile stations will have similar configurations so only one will be described. Thus, the mobile station [0116] where c 4(a) and 4(b), following signal weighting by the power control factor ψ_{pc} ^{u}(t)^{2}, the spread signal is transmitted to the base station 11 via channel 14 ^{u}. FIG. 4(a) shows the “real” situation where the channel characteristics comprise a normalized value H^{u}(T) and a normalization factor ψ_{ch} ^{u}(T) which relates to the “amplitude” or attenuation of the channel, i.e. its square would be proportional to the power divided by the transmitted power. In FIG. 4(a), power control is represented by a multiplier 17 ^{u}, and the subscript “pc”. FIG. 4(b) shows that, for convenience, the channel characteristics can be represented (theoretically) by the normalized value H^{u}(t) and the normalization factor ψ_{ch} ^{u}(t) included in a single power factor ψ^{u}(t) which is equal to ψ_{pc} ^{u}(t)ψ_{ch} ^{u}(t). ψ_{pc} ^{u}(t) is the factor by which the transmitted signal is amplified or attenuated to compensate for channel power gain in ψ_{ch} ^{u}(t) and to maintain the received power (ψ^{u}(t))^{2 }at the required level.
[0117] In such a CDMA system, the signal of each of the mobile stations [0118] At the base station [0119]FIG. 5 illustrates a spatio-temporal array receiver (STAR) for receiving the signal X(t) at the base station [0120] The despreaders [0121] Referring again to FIG. 5, the post-correlation observation vectors [0122] which are supplied to subsequent stages (not shown) of the receiver for processing in known manner. [0123] The STAR modules [0124] The STAR module [0125] The beamformer [0126] The signal component estimate ŝ [0127] The power estimation unit [0128] of the power in that user's signal component ŝ [0129] to the subsequent stages (not shown) of the receiver for derivation of power level adjustment signals in known manner. [0130] The receiver shown in FIG. 5 will perform satisfactorily if there are no strong interferers, i.e., if it can be assumed that all users transmit with the same modulation and at the same rate, and that the base-station knows all the spreading codes of the terminals with which it is communicating. On that basis, operation of the receiver will be described with reference to the user channel identified by index u. [0131] At time t, the antenna array signal vector X(t) received by the elements [0132] where U is the total number of mobile stations whose signals are received at the base-station [0133] where H [0134] of the total power ψ [0135] In the preprocessing unit [0136] where D [0137] It should be noted that the above description is baseband, without loss of generality. Both the carrier frequency modulation and demodulation steps can be embedded in the chip pulse-shaping and matched-filtering operations of Equations (1) and (3), respectively. [0138] Thus, after sampling at the chip rate 1/T [0139] where [0140] In the despreader [0141] Framing this vector over L chip samples at the bit rate forms the post-correlation observation matrix: [0142] The post-correlation data model (PCM) (see reference [ [0143] where Z _{n} ^{u} = H _{n} ^{u} s _{n} ^{u} + N _{PCM,n} ^{u} (8)[0144] To avoid the ambiguity due to a multiplicative factor between [0145] The PCM model significantly reduces inter-symbol interference. It represents an instantaneous mixture model of a narrowband source in a one-dimensional signal subspace and enables exploitation of low complexity narrowband processing methods after despreading. Processing after despreading exploits the processing gain to reduce the interference and to ease its cancellation in subsequent steps by facilitating estimation of channel parameters. [0146] As discussed in reference [ [0147] As shown in FIG. 5, the despreader _{n} ^{u} = Ĥ _{n} ^{u} /M) (i.e. spatio-temporal maximum ratio combining, W _{n} ^{u} ″ Ĥ _{n} ^{u}=1), the STAR module 20 ^{u }provides estimates of signal component s_{n} ^{u}, its DBPSK bit sequence b_{n} ^{u }and its total received power
[0148] as follows: [0149] where α is a smoothing factor. It should be noted that with ad hoc modifications, differential modulation and quasi-coherent differential decoding still apply with DMPSK. Orthogonal modulation can even be detected coherently by STAR without a pilot (references [ _{n+1} ^{M} = Ĥ _{n} ^{u}+μ( Z _{n} ^{u} − Ĥ _{n} ^{u} ŝ _{n} ^{u})ŝ _{n} ^{u}, (12)[0150] where μ is an adaptation step-size. Alternatively, the product {circumflex over (ψ)} [0151] For further information about STAR, the reader is directed to the articles by Affes and Mermelstein identified as references [ [0152] If, as was assumed in reference [ [0153] It is also possible for a receiver of a user within a particular set to cancel “inset” interference from one or more users within the same set; and itself be a contributor to such “inset” interference. Embodiments of the invention applicable to these “outset” and “inset” situations will be described hereinafter. In the description, where a particular user's signal is treated as interference and cancelled, it will be deemed to be a “contributor” and, where a particular user's receiver module receives information to enable it to cancel another user's interference, it will be deemed to be a “recipient”. To simplify the description of the preferred embodiments described herein, it will be assumed that all users employ the same modulation at the same rate. For the purpose of developing the theory of operation, initially it will be assumed that, among the mobile stations in the cell, there will be a first set I of “strong” contributor users, one of which is identified in FIGS. 1 and 2 by index “i”, whose received signal powers are relatively high and hence likely to cause more interference, and a second set D of “low-power” recipient users, one of which is identified in FIGS. 1 and 2 by index “d”, whose received signal powers are relatively low and whose reception may be degraded by interference from the signals from the strong users. In order to receive the low-power users adequately, it usually is desirable to substantially eliminate the interference produced by the high-power users. For simplicity, most of the preferred embodiments of the invention will be described on the basis that the high-power users can be received adequately without interference suppression. It should be appreciated, however, that the “strong” user stations could interfere with each other, in which case one could also apply to any interfering mobile the coloured noise model below and the near-far resistant solution proposed for the low-power user, as will be described later. [0154] Assuming the presence of NI interfering users assigned the indices i=1 to NI, then the spatio-temporal observation vector of any interfering user (u=i∈{1, . . . , NI}) is given from Equation 8 by: _{n} ^{i} = H _{n} ^{i} s _{n} ^{i} + N _{PCM,n} ^{i}, (13)[0155] where [0156] where, in addition to the uncorrelated white noise vector [0157] The receiver shown in FIG. 5 would receive the signals from all of the user stations independently of each other. It should be noted that there is no cross-connection between the receiver modules [0158] In the general case, the total interference [0159] The first constraint provides a substantially distortionless response to the low-power user while the second instantaneously rejects the interference subspace and thereby substantially cancels the total interference. This modification of the beamforming step of STAR will be referred to as interference subspace rejection (ISR). [0160] With an estimate of the constraint matrix Ĉ [0161] where I [0162] Whereas, using the above constraints, the ISR beamformer may process the low-power user's data vector after it has been despread, it is possible, and preferable, to process the data vector without first despreading it. In either case, however, the data vector will still be despread for use by the channel identification unit. Although it is computationally more advantageous to do so without despreading, embodiments of both alternatives will be described. First, however, the spread data model of Equation (2) will be reformulated and developed and then used to derive various modes that implement ISR combining of the data, without despreading, suitable for different complementary situations. [0163] Data Model Without Despreading [0164] The observation matrix Y [0165] where each user u contributes its user-observation matrix Y [0166] Using the fact that any bit-triplet [b [ [0167] the sequence b ^{l} ^{ 0,n }(t)+b _{n−1} ^{u} g ^{l} ^{ −1,n }(t)+b _{n+1} ^{u} g ^{l} ^{ +1,n }(t), (22)[0168] where the indices l [0169] where the canonic user-observation matrices Y ^{l} ^{ cm }(t)c ^{u}(t). (24)[0170] Good approximations of Y [0171] It should be noted that the canonic generating sequences allow more accurate reconstruction (e.g., overlap-add) of time-varying channels. Also, the resulting decomposition in Equation (23) holds for long PN codes. [0172] It should be noted that this decomposition also holds for any complex-valued symbol-triplet [b [0173] With respect to the low-power user assigned the index d and the NI strong interfering mobiles assigned the indices i=1, . . . , NI, the observation vector obtained by reshaping the observation matrix, before despreading, can now be rewritten as: _{n} = Y _{0,n} ^{d} s _{n} ^{d} + I _{ISI,n} ^{d} + I _{n} + N _{n}, (25)[0174] where the first canonic observation vector [0175] is the sum of the interfering signal vectors _{ISI,n} ^{u} =s _{n−1} ^{u} Y _{−1,n} ^{u} +s _{n+1} ^{u} Y _{+1,n} ^{u}, (27)[0176] is the intersymbol interference (ISI) vector of user u. In large processing gain situations, the self ISI vector _{n} = Y _{0,n} ^{d} s _{n} ^{d} + I _{n} + N _{n}, (28)[0177] Despreading the observation vector in the above equation with the spreading sequence of the low-power user d provides the data vector model after despreading in Equation (14). It is possible to derive a finer decomposition of the data model to allow implementation of one or more of the ISR modes over diversities. [0178] Finer Decomposition of the Data Model Over Diversities [0179] Thus, Equation (2a) can be further decomposed over the [0180] The observation signal contribution from the f-th finger is defined as: [0181] where the propagation vector from the f-th finger is: [0182] In the above equation, the scalar γ [0183] Accordingly, after preprocessing, the matched-filtering observation matrix can be decomposed as follows: [0184] where each user u contributes its user-observation matrices Y _{m}δ(t−τ _{p}(t)){circle over (×)}b ^{u}(t)c ^{u}(t), (35)[0185] can be further decomposed over the canonic generating sequences as follows: [0186] where the canonic user-observation matrices Y _{ni}δ(t−τ _{P}(t)){circle over (×)} g ^{l} ^{ 1,n }(t)c ^{u}(t), (37)[0187] where δ(t) denotes the Dirac impulse. Therefore one obtains: [0188] A coarser decomposition over fingers of the total interference vector before despreading defined in Equation (26) gives: [0189] After despreading with the spreading sequence of the low-power user d, it gives: [0190] Embodiments of the invention which use the above decompositions of interference, denoted as ISR-D implementations before and after despreading, will be described later with reference to FIGS. 16 and 23. [0191] ISR Combining Before Despreading [0192] As described hereinbefore, the combining step of STAR is implemented without despreading by replacing Equation (9) for the low-power user with: [0193] where the spatio-temporal beamformer [0194] and C [0195] The constraint matrix without despreading, C [0196] In contrast to the “after despreading” case described earlier, when the data vector is not despread before processing by the ISR combiner (i.e., the constrained spatio-temporal beamformer) [0197] where I _{n} ^{l} ^{ 0,n } c _{l} ^{d}, (46)[0198] the fast convolution with the channel matrix estimate being implemented row-wise with the spread sequence. The symbol {circle over (×)} denotes overlap—add over the past, current and future blocks of the spread sequence to be convolved with a finite-size channel-matrix; hence [0199] It should be noted that, although these ISR modes have formulations that are analogous whether ISR is implemented with or without first despreading the data vector, ISR combining of the data without it first being despread reduces complexity significantly. [0200] Receivers which implement these different ISR modes will now be described, using the same reference numerals for components which are identical or closely similar to those of the receiver of FIG. 5, with a suffix indicating a difference. A generic ISR receiver which does so without despreading of the data will be described first, followed by one which does so after despreading of the data. Thereafter, specific implementations of different ISR modes will be described. [0201] Thus, FIG. 9 illustrates a receiver according to a first embodiment of the invention which comprises a first set I of “strong user” receiver modules _{n}={ _{n} ^{1}, . . . , _{n} ^{N} ^{ c }}. The constraints-set generator 42A may, however, use hypothetical symbol values instead, or a combination of symbol estimates and hypothetical values, as will be described later. Each individual constraint lies in the same observation space as the observation matrix Y_{n }from preprocessor 18. The constraints-set generator 42A supplies the set of constraints _{n }to a constraint matrix generator 43A which uses them to form a constraint matrix Ĉ_{n }and an inverse matrix Q_{n }which supplies it to the beamformer 47 ^{d }and each of the corresponding beamformers in the other receiver modules of set D. The actual content of the set of constraints _{n }and the constraint matrix Ĉ_{n }will depend upon the particular ISR mode being implemented, as will be described later.
[0202] The observation vector deriving means in the receiver of FIG. 9 also comprises a vector reshaper [0203] The STAR module [0204] respectively. The ISR beamformer [0205] As shown in FIG. 10, the constraint matrix generator means _{n} ^{1}, . . . , _{n} ^{N} ^{ c }to form one column of the constraint matrix Ĉ_{n}, which is processed by matrix inverter 49A to form inverse matrix Q_{n}. For simplicity of description, it is implicitly assumed that each of the columns of Ĉ_{n }is normalized to unity when collecting it from the set of constraints _{n}.
[0206] As also illustrated in FIG. 10, beamformer [0207] An alternative configuration of receiver in which the low-power STAR modules of set D implement ISR beamforming after despreading of the observation matrix Y [0208] Referring now to FIG. 12, the common constraint matrix generator means _{n }to form one column of the individual constraint matrix Ĉ_{PCM,n} ^{d }implicitly normalized to unity. The matrix inverter 46B^{d }processes individual constraint matrix Ĉ_{PCM,n} ^{d }to form inverse matrix Q_{PCM,n} ^{d}. The user-specific constraint matrix generator 43B^{d }supplies the constraint matrix Ĉ_{PCM,n} ^{d }and inverse matrix Q_{PCM,n} ^{d }to the coefficient tuning unit 50B^{d }of beamformer 47B^{d}. As shown in FIG. 12, the beamformer 47B^{d }has ML multipliers 51 _{1} ^{d }. . . 51 _{ML} ^{d }which multiply weighting coefficients W _{1,n} ^{d* }. . . W _{ML,n} ^{d* }by elements Z _{1,n} ^{d }. . . Z _{ML,n} ^{d }of the post-correlation observation vector Z _{n} ^{d}. As before, adder 52 ^{d }sums the weighted elements to form the signal component estimate ŝ_{n} ^{d}. The beamformer coefficients are timed according to Equation (18).
[0209] Either of these alternative approaches, i.e. with and without despreading of the data vector supplied to the beamformer, may be used with each of several different ways of implementing the ISR beamforming, i.e. ISR modes. It should be noted that all cases use a constraint matrix which tunes the ISR beamformer to unity response to the desired channel and null response to the interference sub-space. In each case, however, the actual composition of the constraint matrix will differ. [0210] Specific embodiments of the invention implementing the different ISR modes without despreading of the data will now be described with reference to FIGS. [0211] Interference Subspace Rejection Over Total Realisation (ISR-TR) [0212] The receiver unit shown in FIG. 13 is similar to that shown in FIG. 9 in that it comprises a set I of receiver modules _{n} ^{1}, . . . , _{n} ^{NI}, which are supplied to the constraints-set generator 42C comprise the channel vector estimates Ĥ _{n} ^{1}, . . . , Ĥ _{n} ^{NI }and the power estimates {circumflex over (ψ)}_{n} ^{1}, . . . , {circumflex over (ψ)}_{n} ^{NI}, respectively.
[0213] The constraints-set generator [0214] Referring again to FIG. 13 and, as an example, receiver module [0215] It should be noted that the respreaders [0216] The constraint-set generator [0217] The reshaped vector [0218] The beamformer [0219] ISR-TR constitutes the simplest way to characterize the interference subspace, yet the most difficult to achieve accurately; namely by a complete estimation of the instantaneous realization of the total interference vector [0220] where each estimate [0221] For each interfering user assigned the index i=1, . . . , NI, this mode uses estimates of its received power ({circumflex over (ψ)} [0222] In the ISR-TR mode and in the alternative ISR modes to be described hereafter, the interference (due to the strongest users) is first estimated, then eliminated. It should be noted that, although this scheme bears some similarity to prior interference cancellation methods which estimate then subtract the interference, the subtraction makes these prior techniques sensitive to estimation errors. ISR on the other hand rejects interference by beamforming which is robust to estimation errors over the power of the interferers. As one example, ISR-TR would still implement a perfect null-constraint if the power estimates were all biased by an identical multiplicative factor while interference cancellers would subtract the wrong amount of interference. The next mode renders ISR even more robust to power estimation errors. [0223] The receiver illustrated in FIG. 13 may be modified to reduce the information used to generate the interfering signal estimates Ŷ [0224] Interference Subspace Rejection Over Realisations (ISR-R) [0225] In the receiver of FIG. 15, the receiver modules in set I are identical to those of FIG. 13. Receiver module _{n}. In contrast to the receiver of FIG. 13, however, these respread matrices are not summed but rather are processed individually by the constraint matrix generator 43D, which comprises a bank of vector reshapers 48D^{1 }. . . 48D^{NI }and a matrix inverter 49D (not shown but similar to those in FIG. 10). The resulting constraint matrix Ĉ_{n}, comprising the column vectors {circumflex over (Y)}_{n−1} ^{1}, . . . , {circumflex over (Y)}_{n−1} ^{NI }is supplied, together with the corresponding inverse matrix Q_{n}, to each of the receiver modules in set D. Again, only receiver module 20D^{d }is shown, and corresponds to that in the embodiment of FIG. 13. Each of the vectors Ŷ _{n−1} ^{1 }. . . Ŷ _{n−1} ^{NI}, represents an estimate of the interference caused by the corresponding one of the strong interference signals from set I and has the same dimension as the reshaped observation vector Y _{n−1}.
[0226] In this ISR-R mode, the interference subspace is characterized by normalized estimates of the interference vectors [0227] where each estimate [0228] It should be noted that, in the reconstruction of Ŷ [0229] Interference Subspace Rejection Over Diversity (ISR-D) [0230] The ISR-D receiver shown in FIG. 16 is predicated upon the fact that the signal from a particular user will be received by each antenna element via a plurality of sub-paths. Applying the concepts and terminology of so-called RAKE receivers, each sub-path is termed a “finger”. In the embodiments of FIGS. 9, 11, _{n} ^{1 }. . . _{n} ^{NI }comprising a sub-channel vector estimate for each individual sub-channel or finger, to the constraints-set generator 42E. The set of channel estimates _{n} ^{i }comprises the subchannel vector estimates Ĥ_{n} ^{i,1}, . . . , Ĥ_{n} ^{i,N} ^{ f }. The constraints-set generator 42E is similar to that shown in FIG. 15 in that it comprises a bank of respreaders 57 ^{1 }. . . 57 ^{NI }but differs in that the channel replication units 59D^{1 }. . . 59D^{NI }are replaced by sub-channel replication units 59E^{1 }. . . 59E^{NI}, respectively. The sub-channel replication units 59E^{1 }. . . 59E^{NI }convolve the respread symbols with the sub-channel vector estimates Ĥ_{n} ^{1,1 }. . . Ĥ_{n} ^{1,N} ^{ f }; . . . ; Ĥ_{n} ^{NI,1}, . . . , Ĥ_{n} ^{NI,N} ^{ f }respectively, to produce normalized estimates Ŷ_{n−1} ^{1,1 }. . . Ŷ_{n−1} ^{1,N} ^{ f }; . . . ; Ŷ_{n−1} ^{NI,1}, . . . , Ŷ_{n−1} ^{NI,N} ^{ f }of the sub-channel-specific observation matrices decomposed over fingers. Hence, the matrices span the space of their realizations with all possible values of the total received powers (ψ_{n} ^{i})^{2 }and complex channel coefficients ζ_{f,n} ^{i}. The estimates are supplied to a constraint matrix generator 43E which generally is as shown in FIG. 10 and produces the constraint matrix accordingly.
[0231] The constraint matrix Ĉ [0232] Each estimate [0233] It should be noted that, in the reconstruction of Ŷ [0234] It should be noted that, in the receivers of FIGS. 13, 15 and [0235] Interference Subspace Rejection Over Hypotheses (ISR-H) [0236] It is possible to use a set of signals which represent all possible or hypothetical values for the data of the interfering signal. Each of the interfering signals constitutes a vector in a particular domain. It is possible to predict all possible occurrences for the vectors and process all of them in the ISR beamformer and, therefore, virtually guarantee that the real or actual vector will have been nullified. As mentioned, the strong interferers are relatively few, so it is possible, in a practical system, to determine all of the likely positions of the interference vector and compensate or nullify all of them. Such an alternative embodiment, termed Interference Subspace Rejection over Hypotheses (ISR-H) because it uses all possibilities for the realisations, is illustrated in FIG. 17. [0237] The components of the interferer receiver modules of set I, namely the despreaders [0238] Instead, bit sequence generators [0239] The constraint matrix generator [0240] Receiver module [0241] As mentioned above, the two bits adjacent to the processed bit of the i-th interferer contribute in each bit frame to the corresponding interference vector (symbol) to be rejected. As shown in FIG. 18, enumeration of all possible sequences of the processed and adjacent bits gives 2 [0242] It should be appreciated that the bit sequence generators [0243] In the ISR-H embodiment of FIG. 17, the interference subspace is characterized by normalized estimates of the canonic interference vectors {circumflex over ( [0244] where each estimate {circumflex over ( [0245] It should also be noted that, in the reconstruction above, only the channel vector estimates (assumed stationary over the adjacent symbols) are needed for complete interference rejection regardless of any 2D modulation employed (see FIG. 19); hence the extreme robustness expected to power control and bit/symbol errors of interferers. The ISR-H combiner coefficients are symbol-independent and can be computed less frequently when the channel time-variations are slow. [0246] Merging of the D mode with the H mode along the decomposition of Equation (38) yields ISR-HD (hypothesized diversities) with a very close form to the decorrelator. This ISR-HD mode requires a relatively huge number of constraints (i.e., 3N [0247] In fact, it would be desirable to reduce the number of constraints required by the ISR-H receiver described above. This can be done using an intermediate mode which is illustrated in FIG. 20 and in which the receiver modules of both sets I and D are similar to those of FIG. 15; most of their components are identical and have the same reference numbers. In essence, the constraint-set generator [0248] Hence, the beamformer [0249] The receiver of FIG. 20, using what is conveniently referred to as ISR-RH mode for reduced hypotheses over the next interference bits, rejects reduced possibilities of the interference vector realisations. Compared to the receiver of FIG. 17 which uses the ISR-H mode, it is more sensitive to data estimation errors over {circumflex over (b)} [0250] Using the previous and current bit estimates of interferers, uncertainty over the interference subspace can be reduced and it can be characterized by the following matrix of 2NI null-constraints (i.e., N [0251] where: [0252] and where each estimate [0253] The ISR-RH mode has the advantage of reducing the number of null-constraints as compared to the ISR-H mode. A larger number of null-constraints indeed increases complexity, particularly when performing the matrix inversion in Equation (43), and may also result in severe noise enhancement, especially when the processing gain L is low. As the number of strong interferes NI increases in a heavily loaded system, the number of null-constraints (2NI and 3NI) approaches the observation dimension M×(2L−1) and the constraint-matrix may become degenerate. To reduce complexity, guarantee stability in the matrix inversion of Equation (43), and minimize noise enhancement, the constraint matrix Ĉ [0254] to yield the projector Π [0255] In practice, {circumflex over (V)} [0256] Another alternative that reduces noise enhancement constrains the beamformer to implement a close-to-null response towards each null-constraint instead of an exact null-response. This “relaxation” of the null-response leaves more degrees of freedom for ambient noise reduction (i.e., less amplification). It usually amounts to upper-bounding the amplitude of the beamformer response towards each null-constraint to be less than a maximum threshold. This technique is well-known and classified in the literature as a “robust beamforming” method. We can combine it with ISR to reduce noise enhancement. [0257] Constraint relaxation in robust beamforming is usually solved as an optimization problem under constraints using the Lagrange multipliers technique. Without going into the mathematical details of such derivations, we directly provide intuition-based solutions that extend ISR beamforming in a seamless manner. We extend Equations (43) to (45) as follows: [0258] where Λ [0259] The above extended ISR solution reduces to that of Equations (43) to (45) by setting Λ [0260] where R [0261] where C [0262] In the TR mode, interference characterization is quasi-deterministic with: Φ [0263] In the R mode, we have: [0264] while in the D mode, we have: [0265] where ({overscore (ψ)} [0266] Using the inversion lemma, the inverse of R [0267] The MMSE or MVDR beamforming can therefore be identified with the extended ISR solution using: λ={circumflex over (σ)} [0268] and: [0269] The instantaneous estimates in the equation above can be further averaged or smoothed in time. The columns of the constraint matrix estimate Ĉ [0270] Although the MMSE version of ISR serves as a method to reduce noise enhancement, it is still sensitive to hard-decision errors for modes using decision feed-back. Using weights in a different way is a method to reduce sensitivity to hard-decision errors. We notice that the ISR combined signal estimate can be formulated as: [0271] The last reformulation stresses that Ĉ Π [0272] where Λ [0273] This modification means that the interferer is never completely rejected although tentative decisions are all correct; however, the penalty arising from wrong decisions is reduced as well. For ISR-R and ISR-D it can be shown that the optimal weight to be applied to columns representing interferer i is {Λ [0274] It should be noted that each of the receivers of FIGS. 13, 15, [0275] Thus, in the ISR-TR receiver shown in FIG. 21, which corresponds to that shown in FIG. 13, the delay [0276] Receiver module [0277] It should be noted that the despread data vector [0278] As before, the coefficients of the beamformer [0279] where the estimate [0280]FIG. 22 shows a similar modification to the low-power (set D) receiver modules of the “without despreading” ISR-R receiver of FIG. 15. In this case, the output of the constraint-set generator [0281] where each estimate [0282]FIG. 23 illustrates the modification applied to the low-power user receiver module of the ISR-D receiver of FIG. 16. Hence, there is no common matrix inverter. Instead, in the receiver of FIG. 23, each of the receiver modules of set D has a user-specific constraint matrix generator [0283] where each estimate [0284]FIG. 24 illustrates application of the modification to the ISR-H receiver of FIG. 17. Again, the common constraint matrix generator ( [0285] where each estimate [0286] In this case, each of the bit sequence generators [0287] It should be noted that, in any frame of duration [0288]FIG. 26 illustrates application of the modification to the ISR-RH receiver of FIG. 20. Again, the common constraint matrix generator [0289] The user-specific constraint generator [0290] where each pair of estimates [0291] Inter-Symbol Interference (ISI) Rejection [0292] In any of the above-described embodiments of the invention it may be desirable to reduce inter-symbol interference in the receiver modules in set D, especially when low processing gains are involved. As noted in the PCM model where despreading reduces ISI to a negligible amount, for a large processing gain, [0293] Accordingly, it rejects interference and significantly reduces ISI. Complete ISI rejection can be effected by modifying the receiver to make the set of the channel parameter estimators _{n} ^{d }available to the constraints-sets generator 42 for processing in parallel with those of the set I receiver modules. The resulting additional constraint matrix and inverse matrix would also be supplied to the beamformer 47 ^{d }and taken into account when processing the data.
[0294] In such a case, the following matrix can be formed: [0295] and the following 2×2 matrix [0296] inverted to obtain the constrained spatio-temporal beamformer [0297] The projector Π [0298] It should be noted that, if the suppression of strong interferers is not needed, ISI can still be rejected by the following beamformer: [0299] where projector Π [0300] It is also envisaged that the receiver module of FIG. 27 using the MRC beamformer denoted [0301] Pilot-Assisted ISR [0302] STAR-ISR performs blind channel identification within a sign ambiguity (or quantized-phase ambiguity for MPSK). It hence avoids differential demodulation and enables quasi-coherent detection. However, differential decoding is still required at the cost of some loss in performance to resolve the sign ambiguity. To implement full coherent detection and avoid differential decoding, a pilot signal, i.e, a predetermined sequence of symbols known to the receiver (usually a constant “1” sequence), can be sent by the transmitter to enable the receiver to resolve the sign ambiguity. Two pilot types are common namely 1) A pilot-symbol will insert pilot symbols in the data sequence at predetermined symbol positions or iterations n [0303] Pilot signals are usually used to perform channel identification. STAR-ISR achieves this task without a pilot and hence reduces the role of the pilot to a simple resolution of the phase ambiguity resulting from its blind channel identification approach. This new approach to pilot use enables significant reduction of the overhead or power fraction allocated to the pilot, as disclosed in references [ [0304] In FIG. 44, a pilot-symbol assisted ISR receiver is shown for user d. The ISR beamformer [0305] On one hand the data signal component estimates {overscore (s)} [0306] On the other hand the pilot signal component estimates ŝ [0307] In the pilot-symbol assisted ISR receiver of FIG. 44, a conjugator [0308] In FIG. 46, a pilot-channel assisted ISR receiver is shown for user d. To better understand this structure, it is necessary to develop the corresponding data model a step further beforehand. Taking into account the fact that user d uses a pair of spreading codes for pilot and data multiplexing with relative powers ζ [0309] where s [0310] The received pilot and data signals can be seen as two separate users received from the same physical channel (i.e., [0311] In FIG. 46, a data ISR beamformer [0312] On one hand, the data signal component estimates ŝ [0313] Joint ISR Detection [0314] In the foregoing embodiments of the invention, ISR was applied to a selected set D of users, typically users with a low data-rate, who would implement ISR in respect of a selected set I of high-rate users. Although this approach is appropriate in most cases, particularly when the number of high-rate users is very low, there may be cases where the mutual interference caused by other high-rate users is significant, in which case mutual ISR among high-rate users may be desired as well. Such a situation is represented by user sets M [0315] Using Ĉ _{n} ^{i} =Ĉ _{n} Q _{n} R _{3*(i−1)+1}, (71)[0316] where [0317] For the ISR-TR, ISR-R and ISR-D modes, each receiver module, in effect, combines a receiver module of set I with a receiver module of set D, some components being omitted as redundant. Referring again to FIG. 28, which shows such a combined receiver module, the preprocessor _{n−1 }produced by constraint set generator 42P.
[0318] The receiver module [0319] The despreader _{n−1} ^{i}. At the beginning of the processing cycle, the channel identification unit 28P^{i }supplies the spread channel vector estimate Ŷ _{0,n−1} ^{i }to both the ISR beamformer 47P^{i }and the MRC beamformer 27P^{i }for use in updating their coefficients, and supplies the set of channel vector estimates _{n−1} ^{i }to the constraints-set generator 42P.
[0320] The MRC beamformer _{n−1 }it has available the set of channel vector estimates _{n−1}, the “future” symbol estimate {circumflex over (b)}_{MRC,n} ^{i}, the “present” symbol estimate {circumflex over (b)}_{MRC,n−1} ^{i }and the “past” symbol estimate {circumflex over (b)}_{n−2} ^{i}, the latter two from its buffer.
[0321] Each of the other receiver modules in the “joint ISR” set supplies its equivalents of these signals to the constraints-set generator _{n−1 }and supplies the same to the constraint matrix generator 42P, which generates the constraint matrix Ĉ_{n }and the inverse matrix Q_{n }and supplies them to the various receiver modules.
[0322] The constraints-set generator [0323] When the constraints-set generator [0324] An ISR-RH receiver module will use a similar structure, except that the one-bit delay [0325] In order to implement J-ISR, a more general formulation of the constraint matrix is required. The general ISR constraint matrix counting N [0326] where the j-th constraint [0327] where S [0328] and [0329] Ř being the empty set. Table 1 defines the sets S [0330] The objective signal belongs to the total interference subspace as defined by the span of the common constraint matrix Ĉ Π [0331] the desired-signal blocking matrix Ĉ [0332] with S [0333] Joint Multi-User Data and Channel Gain Estimation in ISR-D [0334] Neglecting the signal contributions from the weak-power low-rate users, and limiting to the signals of the NI interferers, [0335] where _{n}. (80)[0336] This constitutes one step of ISR-D operations and allows joint multi-user channel identification. [0337] Multi-Stage ISR Detection [0338] Multi-stage processing may be used in combination with those of the above-described embodiments which use the above-described joint ISR, i.e. all except the receivers implementing ISR-H mode. It should be appreciated that, in each of the receivers which use decision-feedback modes of ISR (TR,R,D,RH), coarse MRC symbol estimates are used in order to reconstruct signals for the ISR operation. Because they are based upon signals which include the interference to be suppressed, the MRC estimates are less reliable than ISR estimates, causing worse reconstruction errors. Better results can be obtained by using multi-stage processing and, in successive stages other than the first, using improved ISR estimates to reconstruct and perform the ISR operation again. [0339] Operation of a multi-stage processing receiver module which would perform several iterations to generate a particular symbol estimate is illustrated in FIG. 29, which depicts the same components, namely constraint-sets generator _{n−1} ^{i }and the current MRC symbol estimate {circumflex over (b)}_{MRC,n} ^{i}. Likewise, the spread channel vector estimate Ŷ _{0,n−1} ^{i }and the delayed observation vector Y _{n−1 }used by the ISR beamformer 47P^{i }will remain the same.
[0340] In iteration [0341] This symbol estimate {circumflex over (b)} [0342] It should be noted that, in FIG. 29, the inputs to the channel identification unit [0343] One stage ISR operation can be generalized as follows: [0344] where ŝ [0345] The multistage approach has a complexity cost; however, complexity can be reduced because many computations from one stage to the next are redundant. For instance, the costly computation [0346] It should be noted that, in the embodiment of FIG. 29, the channel identification unit [0347] ISR Using Multistage with Intermediate Channel Decoding (MICD) [0348] The TURBO channel encoder has recently attracted researchers interest as a new efficient coding structure to achieve Information Bit Error Rates (IBER) close to the Shannon limit. Basically, the strength of the TURBO coding scheme is the concatenation of two convolutional decoders each transmitting the same information data, however, data is temporally organized differently (different interleaving) before it is encoded. From one of the data streams, the decoder provides likelihood estimates to be used as a sort of extrinsic information for decoding of the second stream. [0349] The TURBO idea has recently been generalized to TURBO multiuser receivers. Like the TURBO decoder, the TURBO multiuser principle concatenates detection stages. This idea applies to ISR, and we will name this extension of ISR Multistage with Intermediate Channel Decoding, ISR-MICD. Contrary to multistage ISR, ISR-M, ISR-MICD performs channel decoding between stages as illustrated in FIG. 47. [0350] First MRC beamforming [0351] Group ISR Detection [0352] In practice, the receiver of FIG. 28 could be combined with one of the earlier embodiments to create a receiver for a “hierarchical” situation, i.e., as described hereinbefore with reference to FIG. 8, in which a first group of receiver modules, for the weakest signals, like those in set D of FIG. 8, for example, are “recipients” only, i.e., they do not contribute to the constraint matrix at all; a second group of receiver modules, for the strongest signals, like the receiver modules of set I in FIG. 8, do not need to cancel interference and so are “contributors” only, i.e., they only contribute constraints-sets to the constraint matrix used by other receiver modules; and a third set of receiver modules, for intermediate strength signals, like the receiver modules of sets M [0353] It should be noted that normalization of the columns of Ĉ [0354] A receiver module for set D will set Π [0355] A receiver in set M [0356] Finally, a receiver in set I does not need to cancel any interference. Consequently, it will set both Π [0357] Successive Versus Parallel ISR Detection [0358] Although the embodiments of ISR receivers described hereinbefore use a parallel implementation, ISR may also be implemented in a successive manner, denoted S-ISR, as illustrated in FIG. 30. Assuming implementation of successive ISR among NI interferers, U users, and assuming without loss of generality that users are sorted in order of decreasing strength such that user _{MRC,n} ^{i} ^{ H } Ĉ _{i,n} ^{i} υ _{n}(i), υ _{n}(i)=Q _{n} ^{i} Ĉ _{i,n} ^{H} Y _{n}, (90)[0359] where Ĉ [0360] Hybrid ISR Detection [0361] It should also be appreciated that the different ISR modes may be mixed, conveniently chosen according to the characteristics of their signals or transmission channels, or data rates, resulting hybrid ISR implementations (H-ISR). For example, referring to FIG. 8, the sets I, M [0362] ISR Projection for Enhanced Channel Identification [0363] In all of the above-described embodiments of the invention, the channel identification units [0364] which, in effect, comprises a residual MRC beamformer [0365]FIG. 31 illustrates a modification, applicable to all embodiments of the invention described herein including those described hereafter, which exploits this relationship to improve the spread channel vector estimate [0366] The “cleaned” observation vector [0367] The new “cleaned” vector resulting from the projection of the observation vector [0368] The new observation vector is free from the interferers and ISI and contains a projected version of the channel vector [0369] With respect to the new observation vectors [0370] The equivalence between the two expressions of the beamformer coefficients in Equation (92) due to the nilpotent property of projections should be noted. In more adverse near-far situations, the modification illustrated in FIG. 31 allows more reliable channel identification than simple DFI and hence increases near-far resistance. If necessary, this new DFI version will be termed Π-DFI. It is expected to be suitable for situations where the interferers are moderately strong and when the null constraints cover them all. For simplicity of discussion, projection of the observation will become implicit without reference to [0371] Expanding Dimensionality (X-option) [0372] When the number of users becomes high compared to the processing gain, the dimension of the interference subspace becomes comparable to the total dimension (M(2L−1)). The penalty paid is an often devastating enhancement of the white noise. Unlike ISR-TR, which always requires a single constraint, other DF modes, namely ISR-R and ISR-D, may suffer a large degradation because the number of constraints these modes require easily becomes comparable to the total dimension available. However, the dimension may be increased by using additional data in the observation. This option also allows for asynchronous transmission and for the application of ISR to Mixed Spreading Factor (MSF) systems. [0373] The matched-filtering observation vector [0374] where double underlining stresses the extended model. It should be noted that [0375] As an example, application of the X option to ISR-D, referred to as ISR-DX, requires the following constraint matrix: [0376] The extended vectors in Equation (96) have been treated in the same way as those in Equation (95), i.e., by concatenating reconstructed vectors from consecutive symbols in the extended frame and by implicitly discarding overlapping dimensions in the concatenated vectors. Clearly, extension of the observation space leaves additional degrees of freedom and results in less white noise enhancement. However, it may exact a penalty in the presence of reconstruction errors. [0377] Although the X-option was illustrated in the case of ISR-D, its application to the remaining DF modes is straightforward. It should also be noted that the X-option allows for processing of more than one symbol at each frame while still requiring one matrix inversion only. The duration of the frame, however, should be small compared to the variations of the channel. [0378] In the above-described embodiments, ISR was applied to a quasi-synchronous system where ail temporal delays were limited to 0<τ<L. Although this model reflects well the large processing gain situation, where the limit (L→∞), allows for placing a frame of duration 2L−1 chips which fully cover one bit of all users, including delay spreads. With realistic processing gains, and in particular in the low processing gain situation, this model tends to approach a synchronous scenario. Using the X-option serves as a method supporting complete asynchronous transmission. [0379] Referring to FIG. 32, assuming that the users of the system have processing gain L as usual, the transmitted signal of any user is cyclo-stationary and a possible time-delay of the primary path τ [0380] Multi-Modulation (MM), Multi-Code (MC), and Mixed Spreading Factor (MSF) are technologies that potentially can offer mixed-rate traffic in wideband CDMA. MSF, which has become very timely, was shown to outperform MC in terms of performance and complexity and is also proposed by UMTS third generation mobile system as the mixed-rate scenario. Application of ISR to MSF as the mixed rate scenario considered herein will now be discussed. [0381] In MSF, mixed rate traffic is obtained by assigning different processing gains while using the same carrier and chip-rate. In a system counting two groups of users, a low-rate (LR) and a high rate (HR) group, this means that every time a LR rate user transmits 1 symbol, a HR user transmits 2r+1 HR symbols, r=L [0382] Therefore, fitting the ISR frame subject to LR users or in general the lowest-rate users ensures that also at least r HR symbols are covered when HR and LR have the same delay spread. The ISR generalizes readily to this scenario regarding every HR user as r LR users. In FIG. 33, the grey shaded HR/LR bits symbolize the current bits to be estimated; whereas, former bits have already been estimated (ISR-bits) and future bits are unexplored. It should be noted that current HR bits should be chosen to lie at the end of the frame. [0383] Multi-Code ISR [0384] It is envisaged that a user station could use multiple codes, N [0385] While using all of the multiple codes advantageously gives a more accurate channel vector estimate, it requires many expensive despreading operations. In order to reduce the cost and complexity, the receiver module [0386] It can be demonstrated that the multiple spreading codes can be replaced by a single spreading code formed by multiplying each of the multiple spreading codes by the corresponding one of the symbol estimates {circumflex over (b)} [0387] The theory of such multicode operation will now be developed. Assuming for simplicity that each user assigned the index u transmits N [0388] where the canonic u-th user l-th code observation matrices Y [0389] by: _{m}δ(t−τ _{P}(t))){circle over (×)}g ^{l} ^{ n }(t)c ^{u,l}(t). (98)[0390] In the equation above, [0391] The particularity of the above multi-code model, where N [0392] Considering first joint ISR combining among the group of N interferers, the regular ISR modes, namely TR, R, D, H and RH easily generalize to the new multi-code configuration of N [0393] Although these modes partly implement TR over codes, they are still robust to power estimation errors. Indeed, the fact that the received power of a given user is a common parameter shared between all codes enables its elimination from the columns of the constrain matrices (see Table 3). The MCR and MCD modes inherit the advantages of the R and D modes, respectively. They relatively increase their sensitivity to data estimation errors compared to the original modes, since they accumulate symbol errors over codes. However, they reduce the number of constraints by N [0394] For a desired user assigned the index d, the constraint matrix Ĉ [0395] The projector Π [0396] The above processing organization of ISR among the high-power or low-power user-codes themselves or between both subsets is a particular example that illustrates G-ISR well. The fact that joint ISR among the high-power users and joint ISR among the codes of a particular low-power user may each implement a different mode is another example that illustrates H-ISR well. In the more general case, ISR can implement a composite mode that reduces to a different mode with respect to each user. For instance, within the group of NI interferers, each user-code assigned the index (i,l) can form its own multi-code constraint and blocking matrices Ĉ [0397] This example illustrates the potential flexibility of ISR in designing an optimal interference suppression strategy that would allocate the null constraints among users in the most efficient way to achieve the best performance/complexity tradeoff. It should be noted that, in the particular case where the TR mode is implemented, the matrices in Equations (104) and (105) are in fact vectors which sum the individual multi-code constraint vectors Ĉ [0398] After deriving the beamformer coefficients, each MC user assigned the index u estimates its N [0399] and exploits the fact that it N [0400] It should be noted that the multi-code data-streams can be estimated using MRC, simply by setting the constraint matrices to null matrices. This option will be referred to as MC-MRC. [0401] After despreading of the post-correlation observation vector _{n} ^{u,l} = H _{n} ^{u}ψ_{n} ^{u} b _{n} ^{u,l} + N _{PCM,n} ^{u,l} = H _{n} ^{u} s _{n} ^{u,l} + N _{PCM,n} ^{u,l}. (109)[0402] The fact that all user-codes propagate through the same channel is exploited in the following cooperative channel identification scheme (see FIG. 34): [0403] which implements a modified DFI scheme, referred to as multi-code cooperative DFI (MC-CDFI). MC-CDFI amounts to having the user-codes cooperate in channel identification by estimating their propagation vectors separately, then averaging them over all codes to provide a better channel vector estimate. It should be noted that implicit incorporation of the Π-DFI version in the above MC-CDFI scheme further enhances channel identification. [0404] Since the STAR exploits a data channel as a pilot, it can take advantage of a maximum of N [0405] Another solution that reduces the number of despreading operations reconstructs the following data-modulated cumulative-code after ISR combining and symbol estimation in Equations (106) and (107): [0406] A single despreading operation with this code yields: [0407] It has the advantage of further reducing the noise level by N [0408] This CDFI version is referred to as δ-CDFI (see FIG. 35). [0409] Whereas multicode operation involves user stations transmitting using multiple spreading codes, but usually at the same data rate, it is also envisaged that different users within the same system may transmit at different data rates. It can be demonstrated that the receiver modules shown in FIGS. 34 and 35 need only minor modifications in order to handle multirate transmissions since, as will now be explained, multicode and multirate are essentially interchangeable. [0410] Multi-Code Approach to Multi-Rate ISR [0411] Reconsidering now the conventional MR-CDMA, in this context, STAR-ISR operations previously were implemented at the rate 1/T where T is the symbol duration. As described earlier, with reference to FIG. 32, the “X option” extensions, enables reduction of noise enhancement by increasing the dimension of the observation space and provides larger margin for time-delay tracking in asynchronous transmissions. A complementary approach that decomposes the observation frame into blocks rather than extends it using past reconstructed data will now be described. [0412] This block-processing version of STAR-ISR will still operate at the rate 1/T on data frames barely larger than the processing period T. However, it will decompose each data stream within that frame into data blocks of duration T [0413] In one processing period, STAR-ISR can simultaneously extract or suppress a maximum N [0414] where ␣ [0415] where b [0416] With the above virtual decomposition, one arrives at a MC-CDMA model where each of the processed users can be seen as a mobile that code-multiplexes N [0417] Exploiting this MC approach to MR-CDMA, the data model of MR-CDMA will be developed to reflect a MC-CDMA structure, then a block-processing version of STAR-ISR derived that implements estimation of a symbol fraction or sequence. [0418] The multi-code model of Equation (97) applies immediately to MR-CDMA. However, due to the fact that codes are bursty with duration T [0419] In this frame of duration [0420] where L [0421] where N [0422] where λ [0423] The constraint matrices can be formed in an MC approach to implement joint or user-specific ISR processing in any of the modes described in Tables 3 or 4, respectively. In contrast to the conventional MC-CDMA, the factor λ [0424] After derivation of the beamformer coefficients of each virtual user-code assigned the couple-index (u,l), its signal component s [0425] Indeed, the estimation of the signal components provides sequences oversampled to the resolution rate 1/T [0426] In the particular case where the data rate is equal to the processing rate (i.e., 1/T [0427] If the data rate is slower than the processing rate, the signal component estimate ŝ [0428] Symbol and power estimations in Equations (124) and (125) are on the other hand modified as follows: [0429] It should be noted that a higher value is needed for the smoothing factor α to adapt to a slower update rate of power estimation. If the channel power variations are faster than the data rate, then it is preferable to keep the power estimation update at the processing rate in Equation (125). In this case, Equation (126) is modified as follows [0430] to take into account channel power variations within each symbol duration. [0431] It should be noted that the multi-rate data-streams can be estimated using MRC, simply by setting the constraint matrices to null matrices. This option may be referred to as MR-MRC. [0432] It should be also noted that combination of Equations (106) and (120), along with Equation (128) for data rates slower than the processing rate, successively implements the processing gain of each user in fractioned ISR combining steps. [0433] In general, regrouping the symbol-fractions back to their original rate can be exploited in the design of the constraint matrices; first by reducing reconstruction errors from enhanced decision feedback; and secondly by reducing the number of constraints of a given user u from N [0434] by regrouping the constraint vectors over the user-code indices that restore a complete symbol within the limit of the processing period [0435] Regrouping the constraints of user u to match its original transmission rate amounts to regrouping the codes of this user into a smaller subset that corresponds to a subdivision of its complete code over durations covering its symbol periods instead of the resolution periods. In fact, user u can be characterized by F [0436] This illustrates again the flexibility afforded by using ISR in designing optimal interference suppression strategies that suit well with MR-CDMA. It enables simultaneous processing of blocks of symbols or fractions of symbols in an integrated manner at two common resolution and processing rates. [0437] To carry out channel identification operations, the M×L [0438] where the columns of this matrix are given for j=0, . . . , L [0439] This correlation with the virtual user-code (u,l) amounts to partial despreading by a reduced processing gain L [0440] The reduced-size post-correlation observation vector [0441] Channel identification with the MC-CDFI scheme of Equation (110) can be readily implemented using the user-code post-correlation observation vectors [0442] when the data-rate is faster than the processing rate [0443] in the particular case where the data rate is equal to the processing rate, or by: [0444] when the data-rate is slower than the processing rate. It should be noted that channel identification at data rates faster than the processing gain in Equation (132) has a structure similar to MC-CDFI. Averaging over F [0445] By regrouping codes to match the original data transmission rates as discussed earlier (see FIG. 38), channel identification can be easily reformulated along a mixed MC-CDMA model where each user is characterized by F [0446] To reduce further the number of expensive despreading operations, slower channel identification (reference [ [0447] Although the foregoing embodiments of the invention have been described as receiver modules for a base station, i.e., implementing ISR for the uplink, the invention is equally applicable to the downlink, i.e., to receiver modules of user stations. [0448] Downlink ISR [0449]FIG. 48 illustrates how the downlink can be modelled like an uplink, so that the ISR techniques developed for the uplink can be employed. FIG. 48 shows a single (desired user) mobile [0450] The base stations range from # [0451] There will be other signals from other base stations, some of which are shown in broken lines, and other signals also shown in broken lines, from the base stations shown in full, since FIG. 48 shows only the strongest signals transmitted by each base station. I.e., there will be more than NI mobile stations in cell ν but their transmissions weaker. Those mobiles close to the base station will require weak transmissions whereas those far from the base station will require more power, and the base station power control will increase transmit power to achieve it. Also, the data rates could vary and hence affect power levels. Consequently, many signals are not represented in FIG. 48. They are ignored because they are relatively weak. Of course, they will be part of the noise signal represented in FIG. 2. [0452] Each base station may be transmitting multiple codes, so its signal will have a multi-code structure. Because these signals are being transmitted via the same antenna, they are similar to a multicode signal. Hence, mobile station [0453] The “zoom” inset shows the signal transmitted to a mobile station i by its serving base station ν′ but received as interference by mobile [0454] To implement ISR rejection, the user/mobile station needs to identify the group of users (i.e., interferers) to suppress. Assuming temporarily that suppression is restricted to in-cell users, served by base-station ν, and that the number of suppressed interferers is limited to NI to reduce the number of receivers needed at the desired base-station to detect each of the suppressed users, in order to identify the best users to suppress, the user station can probe the access channels of base-station ν, seeking the NI strongest transmissions. Another scheme would require that the strongest in-cell interfering mobiles cooperate by accessing the first NI channels (i.e. u=i∈{1, . . . , NI}) of base-station ν. [0455] Once the NI suppression channels have been identified, the desired user-station can operate as a “virtual base-station” receiving from NI “mobiles”, i.e., base station transmitter modules, on a “virtual uplink”. If the desired user is not among the NI interferers, an additional user station is considered. Similar NI channels may be identified for transmissions from the neighbouring base-stations. Accordingly, consideration will be given to the NB base-stations, assigned the index ν′∈{1, . . . , NB}, which include the desired base-station with index ν′=ν without loss of generality. This formulation allows the user-station to apply block-processing STAR-ISR with specific adaptations of ISR combining and channel identification to the downlink. [0456] In essence, each “virtual base station” user station would be equipped with a set of receiver modules similar to the receiver modules [0457] It should be appreciated, however, that the signals for other users emanating from a base station are similar to multicode or multirate signals. Consequently, it would be preferable for at least some of the user station receiver modules to implement the multicode or multirate embodiments of the invention with reference to FIGS. 34 and 39. Unlike the base station receiver, the user stations receiver modules usually would not know the data rates of the other users in the system. In some cases, it would be feasible to estimate the data rate from the received signal. Where that was not feasible or desired, however, the multirate or multicode receiver modules described with reference to FIGS. 34 and 39 could need to be modified to dispense with the need to know the data rate. [0458] Referring to FIG. 40, the user station receiver comprises a plurality of receiver modules similar to those of FIG. 39, one for each of the NB base stations whose NI strongest users' signals are to be cancelled, though only one of them, receiver module [0459] i.e. [0460] The resulting signal component estimates ŝ _{n} ^{ν′}, from channel identification unit 28T^{ν′} for use by the constraint matrix generator (not shown) in forming the set of constraints _{n}. The set of channel parameter estimates includes the power estimates from the power estimation units. It should be noted that the constraints-set generator and constraint matrix generator may be described hereinbefore, the actual configuration and operation being determined by the particular ISR mode selected.
[0461] If the desired user, i.e. of the user station receiver [0462] Bearing in mind, however, that the channel vector estimate derived by the receiver module for the serving base station's strong users in FIG. 40 will be for the same channel, but more accurate than the estimate produced by the channel identification unit of FIG. 39, it would be preferable to omit the channel identification unit ( [0463] The receiver module shown in FIG. 40 is predicated upon the data rates of each set of NI users being known to the instant user station receiver. When that is not the case, the receiver module shown in FIG. 40 may be modified as shown in FIG. 42, i.e., by changing the despreaders to segment the code and oversample at a fixed rate that is higher than or equal to the highest data rate that is to be suppressed. [0464] It is also possible to reduce the number of despreading operations performed by the receiver module of FIG. 42 by using a set of compound segment codes as previously described with reference to FIG. 35 to compound over segments. However, as shown in FIG. 43, a set of different compound codes could be used to compound over the set of NI interferers. It would also be possible to combine the embodiment of FIG. 43 with that of FIG. 35 and compound over both the set of interferers and each set of code segments. [0465] A desired user station receiver receiving transmissions on the downlink from its base-station and from the base-stations in the neighbouring cells will now be discussed. Each base-station communicates with the group of user stations located in its cell. Indices ν and u will be used to denote a transmission from base-station ν destined for user u. For simplicity of notation, the index of the desired user station receiving those transmissions will be omitted, all of the signals being implicity observed and processed by that desired user station. [0466] Considering a base-station assigned the index ν, its contribution to the matched-filtering observation vector [0467] where the vector [0468] It should be noted that the channel coefficients ζ [0469] In a first step, the desired user-station estimates the multi-code constraint and blocking matrices of each of the processed in-cell users (i.e., u∈{1, . . . , NI}∪{d}). Table 4 shows how to build these matrices, renamed here as Ĉ [0470] It should be noted that the multi-rate data-streams can be estimated using MRC on the downlink, simply by setting the constraint matrices to null matrices [0471] If the user-station knows [0472] Identification of the propagation channels from each of the interfering base-stations to the desired user station is required to carry out the ISR operations. Considering the in-cell propagation channel, its identification from the post-correlation vectors of the desired user is possible as described hereinbefore with reference to FIG. [0473] If the data rates are known to the base-station, identification of the propagation channel from a given base-station ν′∈{1, . . . ,NB} can be carried out individually from each of its NI interfering users, as described in the previous section. To further enhance channel identification, the resulting individual channel vector estimates are averaged over the interfering users. Both steps combine into one as follows: [0474] This downlink version of MR-CDFI, referred to as DMR-CDFI, is illustrated in FIG. 40. It should be noted that averaging over the interferers takes into account normalization by their total power, To reduce the number of despreading operations, averaging over interferers can be limited to a smaller set ranging between 1 and NI. [0475] If the data rates of the interfering users are unknown to the user-station, identification can be then carried out along the steps described with reference to FIG. 34 to process interfering signals at the common resolution rate as follows: [0476] This downlink version of MC-CDFI, referred to as DMC-CDFI, is illustrated in FIG. 42. To reduce the number of despreading operations, averaging over interferers and user-codes can be limited to smaller subsets ranging between 1 and NI and 1 and N [0477] An alternative solution that reduces the number of despreading operations utilizes the following cumulative multi-codes for l=1, . . . , N [0478] Despreading with these cumulative codes yields: [0479] Averaging the user-codes over interferers does not reduce noise further after despreading. However, the composite signal ŝ [0480] This downlink version of MC-CDFI, referred to as DSMC-CDFI, is illustrated in FIG. 43. Again, averaging over a smaller set of user-codes reduces the number of despreading operations. Use of the δ-CDFI version described in Equations (111) to (113) instead of, or combination with, the above scheme [0481] In situations where a pilot code is transmitted, it can be incorporated into the cumulative code. This version which we denote δπ-MC-CDFI, uses a data modulated cumulative code over multi-codes, interferers, and pilot(s) for despreading. We also introduce normalization by powers in order to meet situations with significant differences in powers (such as on the downlink). δπ-MC-CDFI uses the following code for despreading: [0482] where π, is the spread pilot signal and λ [0483] Moreover, these downlink versions could be combined with the pilot assisted ISR embodiments described with reference to FIGS. 44 and 46. It will be appreciated that the pilot channel will not be user-specific but rather specific to the serving base station or to a group of user stations served by that serving base station. In such cases, the pilot power is relatively strong so the corresponding beamformer (see FIG. 46) may implement simple MRC instead of ISR. [0484] The embodiments of the invention described so far use one transmit (Tx) antenna. Adding spatial dimension using multiple Tx antennas provides a means of supporting more users. In the following section we present a transmitter structure especially suited for high-rate low-processing gain downlink transmission, which can potentially provide high capacity when ISR is employed at the receiver. [0485] Multiple-Input, Multiple Output (MIMO) ISR with Space-Time Coding (STC) [0486] On the uplink, increasing the number of receive (Rx) antennas from one to two almost doubles capacity of the system when ISR is employed at the BS. The improvement results from the additional spatial dimension which allows users to be distinguished not only by their code but also by their spatial signature. When, on the downlink, a single Tx antenna is employed, all signals originating from one specific base station (BS) antenna have the same spatial signature at the antenna array of the receiver. It is therefore a demand that the BS transmitter is equipped with multiple antennas and that a wise space-time coding strategy for transmitting signals is employed. [0487] Such a MIMO transmitter using STC is shown in FIG. 49 for serving base station ν. This figure shows base station ν serving the mobiles indexed from 1 to N [0488] The power-controlled data streams are then fed to a group-selector [0489] For each group, identical operation will be performed; hence only operation for group # [0490] These signals are fed to a spatial mapper [0491] where A [0492] The physical channel matrix which defines transmission from M [0493] where the (ij)-th element of the matrix is the channel between the j-th Tx antenna of the base-station and the i-th Rx antenna of the mobile. This definition allows the signal transmitted from the base-station υ, when received at the antenna array of the desired mobile, to be written as: [0494] where [0495] If the channel as seen by the vector of group signals G [0496] where Γ [0497] from which it is clear that users belonging to the same group of same BS, have the same channel response H [0498] Appropriate choice of codes across groups is important since the resultant channel is not generally orthogonal. Since channelization codes are chosen from a fixed set, sets with good properties can be found by optimization. It should be noted that, since the same scrambling code is used across groups, cross-correlation properties, once set by proper choice of channelization code-sets, are preserved after scrambling and hence after transmission. Here, only the situation with two groups (N [0499] which, in most cases, is easily solved by search. When sets are full (i.e., N [0500] Experience shows that reusing the same or opposite code in different groups is not attractive. The number of users (total number of possible channelization codes) is therefore limited by [0501] because adding more antennas will not provide potential increase of capacity. [0502] The purpose of the spatial mapping function is to assign a unique M [0503] Considering therefore the design of a fixed mapping function, the rank properties of the resultant channel Γ [0504] In the more general case, power control can be distributed among the transmit antennas. Such power control distribution techniques are well known and will not be described in detail here. [0505] It should be noted that embodiments of the invention are not limited to using the space-time coding scheme described with reference to FIG. 49 but could use other known space-time coding schemes. [0506] It will be appreciated that the base station transmitter described with reference to FIG. 49 does not require a modification to the receiver at the user station, i.e., any of the receivers described with reference to FIGS. [0507] It should be noted that each user station could have a plurality of transmit antennas and use a MIMO transmitter similar to that of the base station and described with reference to FIG. 49. Of course, the corresponding receiver at the base station will not require modification for the reasons given above. [0508] [0509] [0510] [0511] [0512] Table 5 shows base-specific constraint and blocking matrices Ĉ [0513] Table 6 shows multi-base constraint and blocking matrices Ĉ [0514] It should be appreciated that, whether ISR is used for the uplink or the downlink, it will function either as a single antenna or multiple antenna for reception or transmission. [0515] Embodiments of the invention are not limited to DBPSK but could provide for practical implementation of ISR in mixed-rate traffic with MPSK or MQAM modulations without increased computing complexity. Even orthogonal Walsh signalling can be implemented at the cost of a computational increase corresponding to the number of Walsh sequences. Moreover, different users could use different modulations. Also, one or more users could use adaptive coding and modulation (ACM). [0516] It is also envisaged that embodiments of the invention could employ carrier frequency offset recovery (CFOR). It should be appreciated that the decision rule units do not have to provide a binary output; they could output the symbol and some other signal state. [0517] It should also be noted that, although the above-described embodiments are asynchronous, a skilled person would be able to apply the invention to synchronous systems without undue experimentation. [0518] The invention comprehends various other modifications to the above-described embodiments. For example, long PN codes could be used, as could large delay-spreads and large inter-user delay-spreads. [0519] For simplicity, the foregoing description of preferred embodiments assumed the use of short spreading codes. In most practical systems, however, long spreading codes would be used. Because the portion of the long code differs from one symbol to the next, certain operations, which were unnecessary for short codes, will have to be performed, as would be appreciated by one skilled in this art. For further information, the reader is directed to references [ [0520] During the acquisition step, a user station could be required to connect using one of a plurality of predetermined (short) codes. The null-constraints used by the receiver then would be preselected to cancel signals using such predetermined codes. This would avoid problems arising when a user station begins to transmit and for which the receiver has not derived any constraints. Such a modification would be applicable to the downlink situation and use ISR-H. [0521] For further information, the reader is directed to the following documents, the contents of which are incorporated herein by reference. [0522] 1. F. Adachi, M. Sawahashi and H. Suda, “Wideband DS-CDMA for next generation mobile communications systems”, IEEE communications Magazine, vol. 36, No. 9, pp. 55-69, September 1998. [0523] 2. A. Duel-Hallen, J. Holtzman, and Z. Zvonar, “Multiuser detection for CDMA systems”, [0524] 3. S. Moshavi, “Multi-user detection for DS-CDMA communications”, [0525] 4. S. Verdu, “Minimum probability of error for asynchronous Gaussian multiple-access channels”, [0526] 5. K. S. Schneider, “Optimum detection of code division multiplexed signals”, [0527] 6. R. Kohno, M. Hatori, and H. Imai, “Cancellation techniques of co-channel interference in asynchronous spread spectrum multiple access systems”, [0528] 7. Z. Xie, R. T. Short, and C. K. Rushforth, “A family of suboptimum detectors for coherent multi-user communications”, [0529] 8. A. J. Viterbi, “Very low rate convolutional codes for maximum theoretical performance of spread-spectrum multiple-access channels”, [0530] 9. M. K. Varanasi and B. Aazhang, “Multistage detection in asynchronous code-division multiple-access communications”, [0531] 10. R. Kohno et al, “Combination of an adaptive array antenna and a canceller of interference for direct-sequence spread-spectrum multiple-access system”, [0532] 11. A. Duel-Hallen, “Decorrelating decision-feedback multi-user detector for synchronous code-division multiple-access channel”, [0533] 12. A. Klein, G. K. Kaleh, and P. W. Baier, “Zero forcing and minimum mean-square-error equalization for multi-user detection in code-division multiple-access channels”, [0534] 13. S. Affes and P. Mermelstein, “A new receiver structure for asynchronous CDMA: STAR—the spatio-temporal array-receiver”, [0535] 14. S. Affes, S. Gazor, and Y. Grenier, “An algorithm for multisource beamforming and multitarget tracking”, [0536] 15. P. Patel and J. Holtzman, “Analysis of a simple successive interference cancellation scheme in a DS/CDMA system”, [0537] 16. J. Choi, “Partial decorrelating detection for DS-CDMA systems”, [0538] 17. S. Affes and P. Mermelstein, “Signal Processing Improvements for Smart Antenna Signals in IS-95 CDMA”, [0539] 18. S. Affes and P. Mermelstein, “Performance of a CDMA beamforming array-receiver in spatially-correlated Rayleigh-fading multipath”, [0540] 19. H. Hansen, S. Affes and P. Mermelstein, “A beamformer for CDMA with enhanced near-far resistance”, [0541] 20. K. Cheikhrouhou, S. Affes, and P. Mermelstein, “Impact of synchronization on receiver performance in wideband CDMA networks”, [0542] 21. S. Affes, A. Louzi, N. Kandil, and P. Mermelstein, “A High Capacity CDMA Array-Receiver Requiring Reduced Pilot Power”, [0543] 22. S. Affes, H. Hansen, and P. Mermelstein, “Interference subspace rejection in wideband CDMA—part I: Modes for mixed power operation”, submitted to JSAC, October 2000. [0544] 23. H. Hansen, S. Affes, and P. Mermelstein, “Interference subspace rejection in wideband CDMA—part II: Modes for high data-rate operation”, submitted to JSAC, October 2000. [0545] 24. E. H. Dinan and B. Jabbari, “Spreading codes for direct sequence CDMA and wideband CDMA cellular networks”, [0546] 25. R. Lupas and S. Verdu, “Near-far resistance of multiuser detectors in asynchronous channels”, [0547] 26. A. Duel-Hallen, “A family of multiuser decision-feedback detectors for asynchronous code-division multiple-access channels”, [0548] 27. C. Schlegel, P. Alexander, and S. Roy, “Coded asynchronous CDMA and its efficient detection”, [0549] 28. L. K. Rasmussen, T. J. Lim, and A. -L. Johansson, “A matrix-algebraic approach to successive interference cancellation in CDMA”, [0550] 29. M. Latva-aho and M. J. Juntti, “LMMSE detection for DS-CDMA systems in fading channels”, [0551] 30. S. Affes, H. Hansen, and P. Mermelstein, “Near-Far Resistant Single-User Channel Identification by Interference Subspace Rejection in Wideband CDMA”, [0552] 31. S. Affes, A. Saadi, and P. Mermelstein, “Pilot-Assisted STAR for Increased Capacity and Coverage on the Downlink of Wideband CDMA Networks”, Referenced by
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